完整後設資料紀錄
DC 欄位語言
dc.contributor.authorYu, Chu-Enen_US
dc.contributor.authorLiu, Chien-Liangen_US
dc.contributor.authorHsieh, Hsin-Lungen_US
dc.date.accessioned2018-08-21T05:56:28Z-
dc.date.available2018-08-21T05:56:28Z-
dc.date.issued2017-01-01en_US
dc.identifier.urihttp://hdl.handle.net/11536/146239-
dc.description.abstractOne of the goals for the machine learning research is to improve the accuracy of the classification. Many research studies have focused on developing novel algorithms according to problem domains and statistical learning theory to continuously improve classification performance over the past decades. Recently, many researchers have found that performance bottleneck often occurs when only using a single classification algorithm, since each algorithm has its strength, but it also has its weakness. Ensemble learning, which combines several classifiers or hypotheses to become a strong classifier or learner, relies on the combination of various hypotheses rather than using state-of-the-art algorithms. In ensemble learning, hypothesis selection is crucial to performance, and the diversity of the selected hypotheses is an important selection criterion. This work proposes three algorithms focusing on generating a hierarchical hypothesis structure to achieve the goal of hypothesis selection, in which the two hypotheses are combined based on particular criterion. We conduct experiments on 8 data sets, and the experimental results indicate that the proposed method outperforms random forest, which is a state-of-the-art method.en_US
dc.language.isoen_USen_US
dc.subjectEnsemble Learningen_US
dc.subjectHypothesis Selectionen_US
dc.subjectHypothesis Divergenceen_US
dc.subjectHypothesis Hierarchical Structureen_US
dc.titleHierarchical Hypothesis Structure for Ensemble Learningen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2017 13TH INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (ICNC-FSKD)en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.department工業工程與管理學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.contributor.departmentDepartment of Industrial Engineering and Managementen_US
dc.identifier.wosnumberWOS:000437355301137en_US
顯示於類別:會議論文