Neutrosophy is the study of neutralities,which is an extension of discussing the truth of opinions.Neutrosophic logic can be applied to any field,to provide the solution for indeterminacy problem.Many of the real-worl...Neutrosophy is the study of neutralities,which is an extension of discussing the truth of opinions.Neutrosophic logic can be applied to any field,to provide the solution for indeterminacy problem.Many of the real-world data have a problem of inconsistency,indeterminacy and incompleteness.Fuzzy sets provide a solution for uncertainties,and intuitionistic fuzzy sets handle incomplete information,but both concepts failed to handle indeterminate information.To handle this complicated situation,researchers require a powerful mathematical tool,naming,neutrosophic sets,which is a generalised concept of fuzzy and intuitionistic fuzzy sets.Neutrosophic sets provide a solution for both incomplete and indeterminate information.It has mainly three degrees of membership such as truth,indeterminacy and falsity.Boolean values are obtained from the three degrees of membership by cut relation method.Data items which contrast from other objects by their qualities are outliers.The weighted density outlier detection method based on rough entropy calculates weights of each object and attribute.From the obtained weighted values,the threshold value is fixed to determine outliers.Experimental analysis of the proposed method has been carried out with neutrosophic movie dataset to detect outliers and also compared with existing methods to prove its performance.展开更多
Purpose-Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance.The data usually follows a biased distribution of classes that ...Purpose-Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance.The data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a dataset.This issue is known as the imbalance problem,which is one of the most common issues occurring in real-time applications.Learning of imbalanced datasets is a ubiquitous challenge in the field of data mining.Imbalanced data degrades the performance of the classifier by producing inaccurate results.Design/methodology/approach-In the proposed work,a novel fuzzy-based Gaussian synthetic minority oversampling(FG-SMOTE)algorithm is proposed to process the imbalanced data.The mechanism of the Gaussian SMOTE technique is based on finding the nearest neighbour concept to balance the ratio between minority and majority class datasets.The ratio of the datasets belonging to the minority and majority class is balanced using a fuzzy-based Levenshtein distance measure technique.Findings-The performance and the accuracy of the proposed algorithm is evaluated using the deep belief networks classifier and the results showed the efficiency of the fuzzy-based Gaussian SMOTE technique achieved an AUC:93.7%.F1 Score Prediction:94.2%,Geometric Mean Score:93.6%predicted from confusion matrix.Research limitations/implications-The proposed research still retains some of the challenges that need to be focused such as application FG-SMOTE to multiclass imbalanced dataset and to evaluate dataset imbalance problem in a distributed environment.Originality/value-The proposed algorithm fundamentally solves the data imbalance issues and challenges involved in handling the imbalanced data.FG-SMOTE has aided in balancing minority and majority class datasets.展开更多
文摘Neutrosophy is the study of neutralities,which is an extension of discussing the truth of opinions.Neutrosophic logic can be applied to any field,to provide the solution for indeterminacy problem.Many of the real-world data have a problem of inconsistency,indeterminacy and incompleteness.Fuzzy sets provide a solution for uncertainties,and intuitionistic fuzzy sets handle incomplete information,but both concepts failed to handle indeterminate information.To handle this complicated situation,researchers require a powerful mathematical tool,naming,neutrosophic sets,which is a generalised concept of fuzzy and intuitionistic fuzzy sets.Neutrosophic sets provide a solution for both incomplete and indeterminate information.It has mainly three degrees of membership such as truth,indeterminacy and falsity.Boolean values are obtained from the three degrees of membership by cut relation method.Data items which contrast from other objects by their qualities are outliers.The weighted density outlier detection method based on rough entropy calculates weights of each object and attribute.From the obtained weighted values,the threshold value is fixed to determine outliers.Experimental analysis of the proposed method has been carried out with neutrosophic movie dataset to detect outliers and also compared with existing methods to prove its performance.
基金Disclosure Statement:No potential conflict of interest was reported by the authors.
文摘Purpose-Adequate resources for learning and training the data are an important constraint to develop an efficient classifier with outstanding performance.The data usually follows a biased distribution of classes that reflects an unequal distribution of classes within a dataset.This issue is known as the imbalance problem,which is one of the most common issues occurring in real-time applications.Learning of imbalanced datasets is a ubiquitous challenge in the field of data mining.Imbalanced data degrades the performance of the classifier by producing inaccurate results.Design/methodology/approach-In the proposed work,a novel fuzzy-based Gaussian synthetic minority oversampling(FG-SMOTE)algorithm is proposed to process the imbalanced data.The mechanism of the Gaussian SMOTE technique is based on finding the nearest neighbour concept to balance the ratio between minority and majority class datasets.The ratio of the datasets belonging to the minority and majority class is balanced using a fuzzy-based Levenshtein distance measure technique.Findings-The performance and the accuracy of the proposed algorithm is evaluated using the deep belief networks classifier and the results showed the efficiency of the fuzzy-based Gaussian SMOTE technique achieved an AUC:93.7%.F1 Score Prediction:94.2%,Geometric Mean Score:93.6%predicted from confusion matrix.Research limitations/implications-The proposed research still retains some of the challenges that need to be focused such as application FG-SMOTE to multiclass imbalanced dataset and to evaluate dataset imbalance problem in a distributed environment.Originality/value-The proposed algorithm fundamentally solves the data imbalance issues and challenges involved in handling the imbalanced data.FG-SMOTE has aided in balancing minority and majority class datasets.
