Ensemble feature selection: Homogeneous and heterogeneous approaches

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• 作者：B. Seijo-Pardo ; ^{borja.seijo@udc.es} ; I. Porto-Dí ; az ^{iporto@udc.es} ; V. Boló ; n-Canedo ^{vbolon@udc.es} ; A. Alonso-Betanzos ^{ciamparo@udc.es}
• 关键词：Ensemble learning ; Feature selection ; Ranking aggregation ; Classification ; SVM-Rank ; Data complexity measures
• 刊名：Knowledge-Based Systems
• 出版年：2017
• 出版时间：15 February 2017
• 年：2017
• 卷：118
• 期：Complete
• 页码：124-139
• 全文大小：1867 K
• 卷排序：118

文摘

Over the last years, ensemble learning has been the focus of much attention. We apply two different designs of ensemble learning on the feature selection process. Homogeneous ensemble distributes the dataset on different nodes. Heterogeneous ensemble combines the result of different feature selection methods. We reduce the training time and release the user to choose a feature selection method.