Ensemble techniques train a set of component classifiers and then combine their predictions to classify new patterns.Bagging is one of the most popular ensemble techniques for improving weak classifiers.However,it is ...Ensemble techniques train a set of component classifiers and then combine their predictions to classify new patterns.Bagging is one of the most popular ensemble techniques for improving weak classifiers.However,it is hard to deploy in many real applications because of the large memory requirement and high computation cost to store and vote the predictions of component classifiers.Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information,which has attracted a lot of attention from theory and application fields.In this paper,a novel rough sets based method is proposed to prune the classifiers obtained from bagging ensemble and select a subset of the component classifiers for aggregation.Experiment results show that the proposed method not only decreases the number of component classifiers but also obtains acceptable performance.展开更多
Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful fo...Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for users.Thus,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid data.This approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.展开更多
基金Supported by the National Natural Science Foundation of China(Granted No.60775036 and No.60475019)the Ph.D.programs Foundation of Ministry of Education of China(No.20060247039)
文摘Ensemble techniques train a set of component classifiers and then combine their predictions to classify new patterns.Bagging is one of the most popular ensemble techniques for improving weak classifiers.However,it is hard to deploy in many real applications because of the large memory requirement and high computation cost to store and vote the predictions of component classifiers.Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information,which has attracted a lot of attention from theory and application fields.In this paper,a novel rough sets based method is proposed to prune the classifiers obtained from bagging ensemble and select a subset of the component classifiers for aggregation.Experiment results show that the proposed method not only decreases the number of component classifiers but also obtains acceptable performance.
基金The research was supported by the National Natural Science Foundation of China under grant No:60775036, 60970061the Higher Education Nature Science Research Fund Project of Jiangsu Province under grant No: 09KJD520004.
文摘Decision rules mining is an important issue in machine learning and data mining.However,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for users.Thus,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid data.This approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.
