With the rapid development of social network in recent years, a huge number of social information has been produced. As traditional recommender systems often face data sparsity and cold-start problem, the use of socia...With the rapid development of social network in recent years, a huge number of social information has been produced. As traditional recommender systems often face data sparsity and cold-start problem, the use of social information has attracted many researchers' attention to improve the prediction accuracy of recommender systems. Social trust and social relation have been proven useful to improve the performance of recommendation. Based on the classic collaborative filtering technique, we propose a PCCTTF recommender method that takes the rating time of users, social trust among users, and item tags into consideration, then do the item recommending. Experimental results show that the PCCTTF method has better prediction accuracy than classical collaborative filtering technique and the state-of-the-art recommender methods, and can also effectively alleviate data sparsity and cold-start problem. Furthermore, the PCCTTF method has better performance than all the compared methods while counting against shilling attacks.展开更多
Using a clinical dataset pertaining to heart illness,this study systematically assesses the generalization ability of many machine learning models.The outcomes show that gradient boosting tree models do exceptionally ...Using a clinical dataset pertaining to heart illness,this study systematically assesses the generalization ability of many machine learning models.The outcomes show that gradient boosting tree models do exceptionally well in terms of generalization in this challenge.With an accuracy of 98.54%,the Light Gradient Boosting Machine(LightGBM)model specifically performed the best because of its strong capacity to extract features from continuous physiological signs.With a 97.56%accuracy rate,the Extreme Gradient Boosting(XGBoost)model demonstrated distinct generalization benefits in recognizing particular disease characteristics.Additionally,the study discovered that by integrating complementary characteristics between the models,model fusion improved decision stability.One example of this is the hybrid model that combines Random Forest and LightGBM(accuracy 98.05%).Conversely,because of the constraints of their model assumptions,traditional linear models like logistic regression(accuracy 79.51%)showed noticeably poorer generalization capacity.The comparative research emphasizes how crucial it is to choose models for intricate clinical prediction tasks that have solid feature representation and excellent nonlinear fitting capabilities.The results offer insightful information about hybrid methodologies and model selection for enhancing the accuracy and dependability of machine learning-based heart disease diagnosis.展开更多
Purpose–The purpose of this paper is to develop a multi-attribute group decision-making(MAGDM)method under the q-rung orthopair trapezoidal fuzzy environment,which calculates the interaction between the criteria depe...Purpose–The purpose of this paper is to develop a multi-attribute group decision-making(MAGDM)method under the q-rung orthopair trapezoidal fuzzy environment,which calculates the interaction between the criteria depending on the proposed q-rung orthopair trapezoidal fuzzy aggregation Choquet integral(q-ROTrFACI)and employ TODIM(an acronym in Portuguese of Interactive and Multi-criteria Decision Making)to consider the risk psychology of decision-makers,to determine the optimal ranking of alternatives.Design/methodology/approach–In MAGDM,q-rung orthopair trapezoidal fuzzy numbers(q-ROTrFNs)are efficient to indicate the quantitative vagueness of decision-makers.The q-ROTrFACI operator is defined and some properties are proved.Then,a novel similarity measure is developed by fusing the area and coordinates of the q-rung orthopair trapezoidal fuzzy function.Based on the above,a Choquet integral-based TODIM(CI-TODIM)method to consider the risk psychology of decision-makers is proposed and two cases are provided to prove superiority of the method.Findings–The paper investigates q-ROTrFACI operator to productively solve problems with interdependent criteria.Then,an approach is proposed to determine the center point of q–ROTrFNs and a q-rung orthopair trapezoidal fuzzy similarity is constructed.Furthermore,CI-TODIM method is devised based on the proposed q-ROTrFACI operator and similarity in q-rung orthopair trapezoidal fuzzy context.The illustration example of business models’solutions and hypertension health management are given to demonstrate the effectiveness and superiority of proposed method.Originality/value–Thepaperdevelops a novelCI-TODIMmethodthat effectivelysolves the MAGDM problems under the premise of fully considering the priority of criteria and the risk preference of decision-makers,which provides guiding advantages for practical decision-making and enriches the application of decision-making theory.展开更多
基金Supported by the National Natural Science Foundation of China(71662014,61602219,71861013)。
文摘With the rapid development of social network in recent years, a huge number of social information has been produced. As traditional recommender systems often face data sparsity and cold-start problem, the use of social information has attracted many researchers' attention to improve the prediction accuracy of recommender systems. Social trust and social relation have been proven useful to improve the performance of recommendation. Based on the classic collaborative filtering technique, we propose a PCCTTF recommender method that takes the rating time of users, social trust among users, and item tags into consideration, then do the item recommending. Experimental results show that the PCCTTF method has better prediction accuracy than classical collaborative filtering technique and the state-of-the-art recommender methods, and can also effectively alleviate data sparsity and cold-start problem. Furthermore, the PCCTTF method has better performance than all the compared methods while counting against shilling attacks.
文摘Using a clinical dataset pertaining to heart illness,this study systematically assesses the generalization ability of many machine learning models.The outcomes show that gradient boosting tree models do exceptionally well in terms of generalization in this challenge.With an accuracy of 98.54%,the Light Gradient Boosting Machine(LightGBM)model specifically performed the best because of its strong capacity to extract features from continuous physiological signs.With a 97.56%accuracy rate,the Extreme Gradient Boosting(XGBoost)model demonstrated distinct generalization benefits in recognizing particular disease characteristics.Additionally,the study discovered that by integrating complementary characteristics between the models,model fusion improved decision stability.One example of this is the hybrid model that combines Random Forest and LightGBM(accuracy 98.05%).Conversely,because of the constraints of their model assumptions,traditional linear models like logistic regression(accuracy 79.51%)showed noticeably poorer generalization capacity.The comparative research emphasizes how crucial it is to choose models for intricate clinical prediction tasks that have solid feature representation and excellent nonlinear fitting capabilities.The results offer insightful information about hybrid methodologies and model selection for enhancing the accuracy and dependability of machine learning-based heart disease diagnosis.
基金This work is funded in part by Department of Shenzhen Local Science and Technology Development(No:2021Szvup052).
文摘Purpose–The purpose of this paper is to develop a multi-attribute group decision-making(MAGDM)method under the q-rung orthopair trapezoidal fuzzy environment,which calculates the interaction between the criteria depending on the proposed q-rung orthopair trapezoidal fuzzy aggregation Choquet integral(q-ROTrFACI)and employ TODIM(an acronym in Portuguese of Interactive and Multi-criteria Decision Making)to consider the risk psychology of decision-makers,to determine the optimal ranking of alternatives.Design/methodology/approach–In MAGDM,q-rung orthopair trapezoidal fuzzy numbers(q-ROTrFNs)are efficient to indicate the quantitative vagueness of decision-makers.The q-ROTrFACI operator is defined and some properties are proved.Then,a novel similarity measure is developed by fusing the area and coordinates of the q-rung orthopair trapezoidal fuzzy function.Based on the above,a Choquet integral-based TODIM(CI-TODIM)method to consider the risk psychology of decision-makers is proposed and two cases are provided to prove superiority of the method.Findings–The paper investigates q-ROTrFACI operator to productively solve problems with interdependent criteria.Then,an approach is proposed to determine the center point of q–ROTrFNs and a q-rung orthopair trapezoidal fuzzy similarity is constructed.Furthermore,CI-TODIM method is devised based on the proposed q-ROTrFACI operator and similarity in q-rung orthopair trapezoidal fuzzy context.The illustration example of business models’solutions and hypertension health management are given to demonstrate the effectiveness and superiority of proposed method.Originality/value–Thepaperdevelops a novelCI-TODIMmethodthat effectivelysolves the MAGDM problems under the premise of fully considering the priority of criteria and the risk preference of decision-makers,which provides guiding advantages for practical decision-making and enriches the application of decision-making theory.
