The influences of number and nature of classifiers on consensus feature selection

Kyriacos Chrysostomou, Sherry Y. Chen, Xiaohui Liu

研究成果: 書貢獻/報告類型會議論文篇章同行評審

摘要

Wrapper feature selection approaches are widely used to select a small subset of relevant features from a dataset. However, Wrappers suffer from the fact that they only use a single classifier when selecting the features. The downside to this approach is that each classifier will have its own biases and will therefore select very different features. In order to overcome the biases of individual classifiers, we propose Consensus Feature Selection (CFS), which combines different classifiers for feature selection. In this way, selecting classifiers for use in the combinations is very important. Therefore, we investigate how the number and nature of classifiers influence the number of features selected and the classification accuracies that these features generate. In terms of number of classifiers, results showed that few selected more relevant features whereas many selected few features. In addition, 3-classifier combinations selected features that led to highest accuracies. In terms of nature of classifiers, decision trees identified most number of features whereas Bayesian classifiers identified least number of features. However, features selected by Bayesian classifiers led to accuracies higher than the other classifiers.

原文???core.languages.en_GB???
主出版物標題Proceedings of the 2008 International Conference on Data Mining, DMIN 2008
編輯R. Stahlbock, S.F. Crone, S. Lessmann
頁面173-179
頁數7
出版狀態已出版 - 2008
事件2008 International Conference on Data Mining, DMIN 2008 - Las Vegas, NV, United States
持續時間: 14 7月 200817 7月 2008

出版系列

名字Proceedings of the 2008 International Conference on Data Mining, DMIN 2008

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???2008 International Conference on Data Mining, DMIN 2008
國家/地區United States
城市Las Vegas, NV
期間14/07/0817/07/08

指紋

深入研究「The influences of number and nature of classifiers on consensus feature selection」主題。共同形成了獨特的指紋。

引用此