Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying ...Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.展开更多
To deal with the data mining problem of asymmetry misclassification cost, an innovative churn prediction method is proposed based on existing churn prediction research. This method adjusts the misclassification cost b...To deal with the data mining problem of asymmetry misclassification cost, an innovative churn prediction method is proposed based on existing churn prediction research. This method adjusts the misclassification cost based on the C4. 5 decision tree as a baseline classifier, which can obtain the prediction model with a minimum error rate based on the assumption that all misclassifications have the same cost, to realize cost-sensitive learning. Results from customer data of a certain Chinese telecommunication company and the fact that the churners and the non-churners have different misclassification costs demonstrate that by altering the sampling ratio of churners and non-churners, this cost-sensitive learning method can considerably reduce the total misclassification cost produced by traditional classification methods. This method can also play an important role in promoting core competence of Chinese telecommunication industry.展开更多
The speed of an Electro-Hydrostatic Actuator(EHA) pump can recently reach 20000 r/min, and its churning losses increase obviously with an increasing speed, which results in low efficiency and thus increasing heat in a...The speed of an Electro-Hydrostatic Actuator(EHA) pump can recently reach 20000 r/min, and its churning losses increase obviously with an increasing speed, which results in low efficiency and thus increasing heat in aircraft EHA systems. In order to reduce churning losses at high speeds, more attention should be given to the design of an insert. In this paper, the effect of an insert with different design parameters on churning losses is investigated through Computational Fluid Dynamics(CFD) simulation and experiments by calculating the difference between churning losses torques of the test pump with and without the insert based on a high-speed churning losses test rig.Analytical results show that the gap between the insert and the cylinder is critical for churning losses reduction. It is found that the churning losses of the test pump can be reduced with a decreasing gap between the cylinder block and the insert at high speeds. This is because the insert can decrease the turbulence occurrence at high speeds. The results can be used for flow field analysis and optimization of the high-speed EHA pump and provide a new method for improving efficiency of high-speed EHA pumps.展开更多
The problem of the churning loss in swash plate axial piston machines is investigated through experimental measurement and theoretical analysis. Several works surrounding churning loss in hydraulic components have bee...The problem of the churning loss in swash plate axial piston machines is investigated through experimental measurement and theoretical analysis. Several works surrounding churning loss in hydraulic components have been proposed in the past, but few have conducted experimental studies and accounted for both dry and wet housing conditions. In this study,a specialized experimental setup is established, which includes a transparent test pump diligently designed for performing various functions of tests. The test pump can work as a real pump without losing any actual features of pump operation. The torque loss in both the dry housing pump and wet housing pump is measured in terms of the shaft speed and its predictive model is also developed analytically. The comparisons between measured and calculated torque loss are presented, showing how speed influences torque loss in both conditions. The advantage/disadvantages of the two cases are summarized. The significance of the test setup is highlighted by verifying the proposed model, which can advance the understanding of energy losses of high speed pumps in future.展开更多
Raising the rotational speed of an axial piston pump is useful for improving its power density;however,the churning losses of the piston increase significantly with increasing speed,and this reduces the performance an...Raising the rotational speed of an axial piston pump is useful for improving its power density;however,the churning losses of the piston increase significantly with increasing speed,and this reduces the performance and efficiency of the axial piston pump.Currently,there has been some research on the churning losses of pistons;however,it has rarely been analyzed from the perspective of the piston number.To improve the performance and efficiency of the axial piston pump,a computational fluid dynamics(CFD)simulation model of the churning loss was established,and the effect of piston number on the churning loss was studied in detail.The simulation analysis results revealed that the churning losses initially increased as the number of pistons increased;however,when the number of pistons increased from six to nine,the torque of the churning losses decreased because of the hydrodynamic shadowing effect.In addition,in the analysis of cavitation results,it was determined that the cavitation area of the axial piston pump was mainly concentrated around the piston,and the cavitation became increasingly severe as the speed increased.By comparing the simulation results with and without the cavitation model,it was observed that the cavitation phenomenon is beneficial for the reduction of churning losses.In this study,a piston churning loss test rig that can eliminate other friction losses was established to verify the accuracy of the simulation results.A comparative analysis indicated that the simulation results were consistent with the actual situation.In addition,this study also conducted a simulation study on seven and nine piston pumps with the same displacement.The simulation results revealed that churning losses of the seven pistons were generally greater than those of the nine pistons under the same displacement.In addition,regarding the same piston number and displacement,reducing the pitch circle radius of piston bores is effective in reducing the churning loss.This research analyzes the effect of piston number on the churning loss,which has certain guiding significance for the structural design and model selection of axial piston pumps.展开更多
Background:Given the importance of customers as the most valuable assets of organizations,customer retention seems to be an essential,basic requirement for any organization.Banks are no exception to this rule.The comp...Background:Given the importance of customers as the most valuable assets of organizations,customer retention seems to be an essential,basic requirement for any organization.Banks are no exception to this rule.The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention.Methods:Being based on existing information technologies which allow one to collect data from organizations’databases,data mining introduces a powerful tool for the extraction of knowledge from huge amounts of data.In this research,the decision tree technique was applied to build a model incorporating this knowledge.Results:The results represent the characteristics of churned customers.Conclusions:Bank managers can identify churners in future using the results of decision tree.They should be provide some strategies for customers whose features are getting more likely to churner’s features.展开更多
The term “customer churn” is used in the industry of information and communication technology (ICT) to indicate those customers who are about to leave for a new competitor, or end their subscription. Predicting this...The term “customer churn” is used in the industry of information and communication technology (ICT) to indicate those customers who are about to leave for a new competitor, or end their subscription. Predicting this behavior is very important for real life market and competition, and it is essential to manage it. In this paper, three hybrid models are investigated to develop an accurate and efficient churn prediction model. The three models are based on two phases;the clustering phase and the prediction phase. In the first phase, customer data is filtered. The second phase predicts the customer behavior. The first model investigates the k-means algorithm for data filtering, and Multilayer Perceptron Artificial Neural Networks (MLP-ANN) for prediction. The second model uses hierarchical clustering with MLP-ANN. The third one uses self organizing maps (SOM) with MLP-ANN. The three models are developed based on real data then the accuracy and churn rate values are calculated and compared. The comparison with the other models shows that the three hybrid models outperformed single common models.展开更多
Keeping customers satisfied is truly essential for saying that business is successful especially in the telecom. Many companies experience different techniques that can predict churn rates and help in designing effect...Keeping customers satisfied is truly essential for saying that business is successful especially in the telecom. Many companies experience different techniques that can predict churn rates and help in designing effective plans for customer retention since the cost of acquiring a new customer is much higher than the cost of retaining the existing one. In this paper, three machine learning algorithms have been used to predict churn namely, Na?ve Bayes, SVM and decision trees using two benchmark datasets IBM Watson dataset, which consist of 7033 observations, 21 attributes and cell2cell dataset that contains 71,047 observations and 57 attributes. The models’ performance has been measured by the area under the curve (AUC) and they scored 0.82, 0.87, 0.77 respectively for IBM dataset and 0.98, 0.99, 0.98 respectively for cell2cell dataset. The proposed models also obtained better accuracy than the previous studies using the same datasets.展开更多
To address the prominent problems faced by customer churn in telecom enterprise management, a telecom customer churn prediction model integrating GA-XGBoost and SHAP is proposed. By using the ADASYN algorithm for data...To address the prominent problems faced by customer churn in telecom enterprise management, a telecom customer churn prediction model integrating GA-XGBoost and SHAP is proposed. By using the ADASYN algorithm for data processing on the unbalanced sample set;based on the GA-XGBoost model, the XGBoost algorithm is used to construct the telecom customer churn prediction model, and the hyperparameters of the model are optimized by using the genetic algorithm. The experimental results show that compared with traditional machine learning methods such as GBDT, decision tree, KNN and single XGBoost model, the improved XGBoost model has better performance in recall, F1 value and AUC value;the GA-XGBoost model is integrated with SHAP framework to analyze and explain the important features affecting telecom customer churn, which is more in line with the telecom industry to predict customer the actual situation of churn.展开更多
As the cost of accessing a telecom operator’s network continues to decrease,user churn after arrears occurred repeatedly,which has brought huge economic losses to operators and reminded them that it is significant to...As the cost of accessing a telecom operator’s network continues to decrease,user churn after arrears occurred repeatedly,which has brought huge economic losses to operators and reminded them that it is significant to identify users who are likely to churn in advance.Machine learning can form a series of judgment rules by summarizing a large amount of data,and telecom user data naturally has the advantage of user scale,which can provide data support for learning algorithms.XGBoost is an improved gradient boosting algorithm,and in this paper,we explore how to use the algorithm to train an efficient model and use this model one month in advance to predict whether users will churn.Our work is mainly divided into two aspects:(1)By completing data exploration,feature engineering and data preprocessing,we obtained a data set that can be used to train a prediction model and features that can effectively predict user churn.And using these features and data sets,two prediction models were trained based on Random Forest and XGBoost.(2)According to the business needs of telecom operators,we continuously evaluated and optimized these models.And by comparing the test results of the two models,we proved that the XGBoost model performs better for the precision and recall of user churn.展开更多
Recently, it has been seen that the ensemble classifier is an effective way to enhance the prediction performance. However, it usually suffers from the problem of how to construct an appropriate classifier based on a ...Recently, it has been seen that the ensemble classifier is an effective way to enhance the prediction performance. However, it usually suffers from the problem of how to construct an appropriate classifier based on a set of complex data, for example,the data with many dimensions or hierarchical attributes. This study proposes a method to constructe an ensemble classifier based on the key attributes. In addition to its high-performance on precision shared by common ensemble classifiers, the calculation results are highly intelligible and thus easy for understanding.Furthermore, the experimental results based on the real data collected from China Mobile show that the keyattributes-based ensemble classifier has the good performance on both of the classifier construction and the customer churn prediction.展开更多
As the banking industry gradually steps into the digital era of Bank 4.0,business competition is becoming increasingly fierce,and banks are also facing the problem of massive customer churn.To better maintain their cu...As the banking industry gradually steps into the digital era of Bank 4.0,business competition is becoming increasingly fierce,and banks are also facing the problem of massive customer churn.To better maintain their customer resources,it is crucial for banks to accurately predict customers with a tendency to churn.Aiming at the typical binary classification problem like customer churn,this paper establishes an early-warning model for credit card customer churn.That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm(GSA)and an Improved Beetle Antennae Search(IBAS)is proposed to optimize the parameters of the CatBoost algorithm,which forms the GSAIBAS-CatBoost model.Especially,considering that the BAS algorithm has simple parameters and is easy to fall into local optimum,the Sigmoid nonlinear convergence factor and the lane flight equation are introduced to adjust the fixed step size of beetle.Then this improved BAS algorithm with variable step size is fused with the GSA to form a GSAIBAS algorithm which can achieve dual optimization.Moreover,an empirical analysis is made according to the data set of credit card customers from Analyttica official platform.The empirical results show that the values of Area Under Curve(AUC)and recall of the proposedmodel in this paper reach 96.15%and 95.56%,respectively,which are significantly better than the other 9 common machine learning models.Compared with several existing optimization algorithms,GSAIBAS algorithm has higher precision in the parameter optimization for CatBoost.Combined with two other customer churn data sets on Kaggle data platform,it is further verified that the model proposed in this paper is also valid and feasible.展开更多
With the increasing requirements of electro-hydrostatic actuators(EHAs)for power,volume,and pressure,there is a growing tendency in the industry to combine the motor and pump to form a so-called'motor pump'to ...With the increasing requirements of electro-hydrostatic actuators(EHAs)for power,volume,and pressure,there is a growing tendency in the industry to combine the motor and pump to form a so-called'motor pump'to improve the integration.In this paper,a novel structure for a wet three-phase high-speed reluctance motor pump is proposed,which can further improve integration by removing the dynamic seal on the pump shaft,thereby avoiding the problems of dynamic seal wear and oil leakage and improving heat dissipation under high-speed working conditions.However,after the motor is wetted,the churning loss caused by immersion of the rotor in the oil causes additional fluid resistance torque.Based on fundamental fluid mechanics,an analytical model of the churning torque of a wet motor was established.To verify the accuracy of the analytical model,a simulation model of churning loss was established based on computational fluid dynamics(CFD),and the churning torque and flow field state were analyzed.Finally,an experimental prototype was designed and manufactured,and a test bench for churning loss was built.The oil churning torque was measured at different speeds and temperatures.The results from the analytical,simulation,and experimental models agreed well.The experimental results validated the analytical model and CFD simulation.This research provides a practical method for calculating the churning loss and serves as guidance for future optimization of churning loss reduction.展开更多
Presently,customer retention is essential for reducing customer churn in telecommunication industry.Customer churn prediction(CCP)is important to predict the possibility of customer retention in the quality of service...Presently,customer retention is essential for reducing customer churn in telecommunication industry.Customer churn prediction(CCP)is important to predict the possibility of customer retention in the quality of services.Since risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer behavior.Besides,deep learning(DL)models help in prediction of the customer behavior based characteristic data.Since the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business people.In this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application sector.In addition,the O-DCCAEP method purposes for determining the churning nature of the customers.The O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter optimization.Additionally,the DCCAE model is employed to classify the churners or non-churner.Furthermore,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.展开更多
In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingex...In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingexisting ones. The success of retention initiatives is determined not only bythe accuracy of forecasting churners but also by the timing of the forecast.Previous works on churn forecast presented models for anticipating churnquarterly or monthly with an emphasis on customers’ static behavior. Thispaper’s objective is to calculate daily churn based on dynamic variations inclient behavior. Training excellent models to further identify potential churningcustomers helps insurance companies make decisions to retain customerswhile also identifying areas for improvement. Thus, it is possible to identifyand analyse clients who are likely to churn, allowing for a reduction in thecost of support and maintenance. Binary Golden Eagle Optimizer (BGEO)is used to select optimal features from the datasets in a preprocessing step.As a result, this research characterized the customer’s daily behavior usingvarious models such as RFM (Recency, Frequency, Monetary), MultivariateTime Series (MTS), Statistics-based Model (SM), Survival analysis (SA),Deep learning (DL) based methodologies such as Recurrent Neural Network(RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU),and Customized Extreme Learning Machine (CELM) are framed the problemof daily forecasting using this description. It can be concluded that all modelsproduced better overall outcomes with only slight variations in performancemeasures. The proposed CELM outperforms all other models in terms ofaccuracy (96.4).展开更多
Telecom industry relies on churn prediction models to retain their customers.These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers.Re...Telecom industry relies on churn prediction models to retain their customers.These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers.Retention not only contributes to the profit of an organization,but it is also important for upholding a position in the competitive market.In the past,numerous churn prediction models have been proposed,but the current models have a number of flaws that prevent them from being used in real-world largescale telecom datasets.These schemes,fail to incorporate frequently changing requirements.Data sparsity,noisy data,and the imbalanced nature of the dataset are the other main challenges for an accurate prediction.In this paper,we propose a hybrid model,name as“A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning(HCPRs)”that used Synthetic Minority Over-Sampling Technique(SMOTE)and Particle Swarm Optimization(PSO)to address the issue of imbalance class data and feature selection.Data cleaning and normalization has been done on big Orange dataset contains 15000 features along with 50000 entities.Substantial experiments are performed to test and validate the model on Random Forest(RF),Linear Regression(LR),Naïve Bayes(NB)and XG-Boost.Results show that the proposed model when used with XGBoost classifier,has greater Accuracy Under Curve(AUC)of 98%as compared with other methods.展开更多
文摘Customer churn is the rate at which customers discontinue doing business with a company over a given time period.It is an essential measure for businesses to monitor high churn rates,as they often indicate underlying issues with services,products,or customer experience,resulting in considerable income loss.Prediction of customer churn is a crucial task aimed at retaining customers and maintaining revenue growth.Traditional machine learning(ML)models often struggle to capture complex temporal dependencies in client behavior data.To address this,an optimized deep learning(DL)approach using a Regularized Bidirectional Long Short-Term Memory(RBiLSTM)model is proposed to mitigate overfitting and improve generalization error.The model integrates dropout,L2-regularization,and early stopping to enhance predictive accuracy while preventing over-reliance on specific patterns.Moreover,this study investigates the effect of optimization techniques on boosting the training efficiency of the developed model.Experimental results on a recent public customer churn dataset demonstrate that the trained model outperforms the traditional ML models and some other DL models,such as Long Short-Term Memory(LSTM)and Deep Neural Network(DNN),in churn prediction performance and stability.The proposed approach achieves 96.1%accuracy,compared with LSTM and DNN,which attain 94.5%and 94.1%accuracy,respectively.These results confirm that the proposed approach can be used as a valuable tool for businesses to identify at-risk consumers proactively and implement targeted retention strategies.
文摘To deal with the data mining problem of asymmetry misclassification cost, an innovative churn prediction method is proposed based on existing churn prediction research. This method adjusts the misclassification cost based on the C4. 5 decision tree as a baseline classifier, which can obtain the prediction model with a minimum error rate based on the assumption that all misclassifications have the same cost, to realize cost-sensitive learning. Results from customer data of a certain Chinese telecommunication company and the fact that the churners and the non-churners have different misclassification costs demonstrate that by altering the sampling ratio of churners and non-churners, this cost-sensitive learning method can considerably reduce the total misclassification cost produced by traditional classification methods. This method can also play an important role in promoting core competence of Chinese telecommunication industry.
基金financial supports from the National Basic Research Program of China(973 Program)(No.2014CB046403)the National Natural Science Foundation of China(No.1737110)
文摘The speed of an Electro-Hydrostatic Actuator(EHA) pump can recently reach 20000 r/min, and its churning losses increase obviously with an increasing speed, which results in low efficiency and thus increasing heat in aircraft EHA systems. In order to reduce churning losses at high speeds, more attention should be given to the design of an insert. In this paper, the effect of an insert with different design parameters on churning losses is investigated through Computational Fluid Dynamics(CFD) simulation and experiments by calculating the difference between churning losses torques of the test pump with and without the insert based on a high-speed churning losses test rig.Analytical results show that the gap between the insert and the cylinder is critical for churning losses reduction. It is found that the churning losses of the test pump can be reduced with a decreasing gap between the cylinder block and the insert at high speeds. This is because the insert can decrease the turbulence occurrence at high speeds. The results can be used for flow field analysis and optimization of the high-speed EHA pump and provide a new method for improving efficiency of high-speed EHA pumps.
基金Supported by the National Natural Science Foundation of China(51005030)The Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems(201718)
文摘The problem of the churning loss in swash plate axial piston machines is investigated through experimental measurement and theoretical analysis. Several works surrounding churning loss in hydraulic components have been proposed in the past, but few have conducted experimental studies and accounted for both dry and wet housing conditions. In this study,a specialized experimental setup is established, which includes a transparent test pump diligently designed for performing various functions of tests. The test pump can work as a real pump without losing any actual features of pump operation. The torque loss in both the dry housing pump and wet housing pump is measured in terms of the shaft speed and its predictive model is also developed analytically. The comparisons between measured and calculated torque loss are presented, showing how speed influences torque loss in both conditions. The advantage/disadvantages of the two cases are summarized. The significance of the test setup is highlighted by verifying the proposed model, which can advance the understanding of energy losses of high speed pumps in future.
基金National Natural Science Foundation of China(Grant No.52005429)Open Foundation of State Key Laboratory of Fluid Power and Mechatronic Systems of China(Grant No.GZKF-201911)National Key Research and Development Program of China(Grant No.2018YFB2000703).
文摘Raising the rotational speed of an axial piston pump is useful for improving its power density;however,the churning losses of the piston increase significantly with increasing speed,and this reduces the performance and efficiency of the axial piston pump.Currently,there has been some research on the churning losses of pistons;however,it has rarely been analyzed from the perspective of the piston number.To improve the performance and efficiency of the axial piston pump,a computational fluid dynamics(CFD)simulation model of the churning loss was established,and the effect of piston number on the churning loss was studied in detail.The simulation analysis results revealed that the churning losses initially increased as the number of pistons increased;however,when the number of pistons increased from six to nine,the torque of the churning losses decreased because of the hydrodynamic shadowing effect.In addition,in the analysis of cavitation results,it was determined that the cavitation area of the axial piston pump was mainly concentrated around the piston,and the cavitation became increasingly severe as the speed increased.By comparing the simulation results with and without the cavitation model,it was observed that the cavitation phenomenon is beneficial for the reduction of churning losses.In this study,a piston churning loss test rig that can eliminate other friction losses was established to verify the accuracy of the simulation results.A comparative analysis indicated that the simulation results were consistent with the actual situation.In addition,this study also conducted a simulation study on seven and nine piston pumps with the same displacement.The simulation results revealed that churning losses of the seven pistons were generally greater than those of the nine pistons under the same displacement.In addition,regarding the same piston number and displacement,reducing the pitch circle radius of piston bores is effective in reducing the churning loss.This research analyzes the effect of piston number on the churning loss,which has certain guiding significance for the structural design and model selection of axial piston pumps.
文摘Background:Given the importance of customers as the most valuable assets of organizations,customer retention seems to be an essential,basic requirement for any organization.Banks are no exception to this rule.The competitive atmosphere within which electronic banking services are provided by different banks increases the necessity of customer retention.Methods:Being based on existing information technologies which allow one to collect data from organizations’databases,data mining introduces a powerful tool for the extraction of knowledge from huge amounts of data.In this research,the decision tree technique was applied to build a model incorporating this knowledge.Results:The results represent the characteristics of churned customers.Conclusions:Bank managers can identify churners in future using the results of decision tree.They should be provide some strategies for customers whose features are getting more likely to churner’s features.
文摘The term “customer churn” is used in the industry of information and communication technology (ICT) to indicate those customers who are about to leave for a new competitor, or end their subscription. Predicting this behavior is very important for real life market and competition, and it is essential to manage it. In this paper, three hybrid models are investigated to develop an accurate and efficient churn prediction model. The three models are based on two phases;the clustering phase and the prediction phase. In the first phase, customer data is filtered. The second phase predicts the customer behavior. The first model investigates the k-means algorithm for data filtering, and Multilayer Perceptron Artificial Neural Networks (MLP-ANN) for prediction. The second model uses hierarchical clustering with MLP-ANN. The third one uses self organizing maps (SOM) with MLP-ANN. The three models are developed based on real data then the accuracy and churn rate values are calculated and compared. The comparison with the other models shows that the three hybrid models outperformed single common models.
文摘Keeping customers satisfied is truly essential for saying that business is successful especially in the telecom. Many companies experience different techniques that can predict churn rates and help in designing effective plans for customer retention since the cost of acquiring a new customer is much higher than the cost of retaining the existing one. In this paper, three machine learning algorithms have been used to predict churn namely, Na?ve Bayes, SVM and decision trees using two benchmark datasets IBM Watson dataset, which consist of 7033 observations, 21 attributes and cell2cell dataset that contains 71,047 observations and 57 attributes. The models’ performance has been measured by the area under the curve (AUC) and they scored 0.82, 0.87, 0.77 respectively for IBM dataset and 0.98, 0.99, 0.98 respectively for cell2cell dataset. The proposed models also obtained better accuracy than the previous studies using the same datasets.
文摘To address the prominent problems faced by customer churn in telecom enterprise management, a telecom customer churn prediction model integrating GA-XGBoost and SHAP is proposed. By using the ADASYN algorithm for data processing on the unbalanced sample set;based on the GA-XGBoost model, the XGBoost algorithm is used to construct the telecom customer churn prediction model, and the hyperparameters of the model are optimized by using the genetic algorithm. The experimental results show that compared with traditional machine learning methods such as GBDT, decision tree, KNN and single XGBoost model, the improved XGBoost model has better performance in recall, F1 value and AUC value;the GA-XGBoost model is integrated with SHAP framework to analyze and explain the important features affecting telecom customer churn, which is more in line with the telecom industry to predict customer the actual situation of churn.
基金This work was supported by the National Natural Science Foundation of China(61871046).
文摘As the cost of accessing a telecom operator’s network continues to decrease,user churn after arrears occurred repeatedly,which has brought huge economic losses to operators and reminded them that it is significant to identify users who are likely to churn in advance.Machine learning can form a series of judgment rules by summarizing a large amount of data,and telecom user data naturally has the advantage of user scale,which can provide data support for learning algorithms.XGBoost is an improved gradient boosting algorithm,and in this paper,we explore how to use the algorithm to train an efficient model and use this model one month in advance to predict whether users will churn.Our work is mainly divided into two aspects:(1)By completing data exploration,feature engineering and data preprocessing,we obtained a data set that can be used to train a prediction model and features that can effectively predict user churn.And using these features and data sets,two prediction models were trained based on Random Forest and XGBoost.(2)According to the business needs of telecom operators,we continuously evaluated and optimized these models.And by comparing the test results of the two models,we proved that the XGBoost model performs better for the precision and recall of user churn.
基金supported by the National Natural Science Foundation of China under Grants No.71271044 and 71572029
文摘Recently, it has been seen that the ensemble classifier is an effective way to enhance the prediction performance. However, it usually suffers from the problem of how to construct an appropriate classifier based on a set of complex data, for example,the data with many dimensions or hierarchical attributes. This study proposes a method to constructe an ensemble classifier based on the key attributes. In addition to its high-performance on precision shared by common ensemble classifiers, the calculation results are highly intelligible and thus easy for understanding.Furthermore, the experimental results based on the real data collected from China Mobile show that the keyattributes-based ensemble classifier has the good performance on both of the classifier construction and the customer churn prediction.
基金This work is supported by the National Natural Science Foundation of China(Nos.72071150,71871174).
文摘As the banking industry gradually steps into the digital era of Bank 4.0,business competition is becoming increasingly fierce,and banks are also facing the problem of massive customer churn.To better maintain their customer resources,it is crucial for banks to accurately predict customers with a tendency to churn.Aiming at the typical binary classification problem like customer churn,this paper establishes an early-warning model for credit card customer churn.That is a dual search algorithm named GSAIBAS by incorporating Golden Sine Algorithm(GSA)and an Improved Beetle Antennae Search(IBAS)is proposed to optimize the parameters of the CatBoost algorithm,which forms the GSAIBAS-CatBoost model.Especially,considering that the BAS algorithm has simple parameters and is easy to fall into local optimum,the Sigmoid nonlinear convergence factor and the lane flight equation are introduced to adjust the fixed step size of beetle.Then this improved BAS algorithm with variable step size is fused with the GSA to form a GSAIBAS algorithm which can achieve dual optimization.Moreover,an empirical analysis is made according to the data set of credit card customers from Analyttica official platform.The empirical results show that the values of Area Under Curve(AUC)and recall of the proposedmodel in this paper reach 96.15%and 95.56%,respectively,which are significantly better than the other 9 common machine learning models.Compared with several existing optimization algorithms,GSAIBAS algorithm has higher precision in the parameter optimization for CatBoost.Combined with two other customer churn data sets on Kaggle data platform,it is further verified that the model proposed in this paper is also valid and feasible.
基金This work is supported by the National Key R&D Program of China(No.2019YFB2005202).
文摘With the increasing requirements of electro-hydrostatic actuators(EHAs)for power,volume,and pressure,there is a growing tendency in the industry to combine the motor and pump to form a so-called'motor pump'to improve the integration.In this paper,a novel structure for a wet three-phase high-speed reluctance motor pump is proposed,which can further improve integration by removing the dynamic seal on the pump shaft,thereby avoiding the problems of dynamic seal wear and oil leakage and improving heat dissipation under high-speed working conditions.However,after the motor is wetted,the churning loss caused by immersion of the rotor in the oil causes additional fluid resistance torque.Based on fundamental fluid mechanics,an analytical model of the churning torque of a wet motor was established.To verify the accuracy of the analytical model,a simulation model of churning loss was established based on computational fluid dynamics(CFD),and the churning torque and flow field state were analyzed.Finally,an experimental prototype was designed and manufactured,and a test bench for churning loss was built.The oil churning torque was measured at different speeds and temperatures.The results from the analytical,simulation,and experimental models agreed well.The experimental results validated the analytical model and CFD simulation.This research provides a practical method for calculating the churning loss and serves as guidance for future optimization of churning loss reduction.
文摘Presently,customer retention is essential for reducing customer churn in telecommunication industry.Customer churn prediction(CCP)is important to predict the possibility of customer retention in the quality of services.Since risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer behavior.Besides,deep learning(DL)models help in prediction of the customer behavior based characteristic data.Since the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business people.In this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application sector.In addition,the O-DCCAEP method purposes for determining the churning nature of the customers.The O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter optimization.Additionally,the DCCAE model is employed to classify the churners or non-churner.Furthermore,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.
文摘In the insurance sector, a massive volume of data is being generatedon a daily basis due to a vast client base. Decision makers and businessanalysts emphasized that attaining new customers is costlier than retainingexisting ones. The success of retention initiatives is determined not only bythe accuracy of forecasting churners but also by the timing of the forecast.Previous works on churn forecast presented models for anticipating churnquarterly or monthly with an emphasis on customers’ static behavior. Thispaper’s objective is to calculate daily churn based on dynamic variations inclient behavior. Training excellent models to further identify potential churningcustomers helps insurance companies make decisions to retain customerswhile also identifying areas for improvement. Thus, it is possible to identifyand analyse clients who are likely to churn, allowing for a reduction in thecost of support and maintenance. Binary Golden Eagle Optimizer (BGEO)is used to select optimal features from the datasets in a preprocessing step.As a result, this research characterized the customer’s daily behavior usingvarious models such as RFM (Recency, Frequency, Monetary), MultivariateTime Series (MTS), Statistics-based Model (SM), Survival analysis (SA),Deep learning (DL) based methodologies such as Recurrent Neural Network(RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU),and Customized Extreme Learning Machine (CELM) are framed the problemof daily forecasting using this description. It can be concluded that all modelsproduced better overall outcomes with only slight variations in performancemeasures. The proposed CELM outperforms all other models in terms ofaccuracy (96.4).
文摘Telecom industry relies on churn prediction models to retain their customers.These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers.Retention not only contributes to the profit of an organization,but it is also important for upholding a position in the competitive market.In the past,numerous churn prediction models have been proposed,but the current models have a number of flaws that prevent them from being used in real-world largescale telecom datasets.These schemes,fail to incorporate frequently changing requirements.Data sparsity,noisy data,and the imbalanced nature of the dataset are the other main challenges for an accurate prediction.In this paper,we propose a hybrid model,name as“A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning(HCPRs)”that used Synthetic Minority Over-Sampling Technique(SMOTE)and Particle Swarm Optimization(PSO)to address the issue of imbalance class data and feature selection.Data cleaning and normalization has been done on big Orange dataset contains 15000 features along with 50000 entities.Substantial experiments are performed to test and validate the model on Random Forest(RF),Linear Regression(LR),Naïve Bayes(NB)and XG-Boost.Results show that the proposed model when used with XGBoost classifier,has greater Accuracy Under Curve(AUC)of 98%as compared with other methods.
