教師資料查詢 | 類別: 期刊論文 | 教師: 陳昆皇 KUN-HUANG CHEN (瀏覽個人網頁)

標題:A new particle swarm feature selection method for classification
學年102
學期2
出版(發表)日期2014/06/01
作品名稱A new particle swarm feature selection method for classification
作品名稱(其他語言)
著者K.-H. Chen; L.-F. Chen; C.-T. Su
單位
出版者
著錄名稱、卷期、頁數Journal of Intelligent Information Systems 42(3), pp.507-530
摘要Searching for an optimal feature subset from a high-dimensional feature space is an NP-complete problem; hence, traditional optimization algorithms are inefficient when solving large-scale feature selection problems. Therefore, meta-heuristic algorithms are extensively adopted to solve such problems efficiently. This study proposes a regression-based particle swarm optimization for feature selection problem. The proposed algorithm can increase population diversity and avoid local optimal trapping by improving the jump ability of flying particles. The data sets collected from UCI machine learning databases are used to evaluate the effectiveness of the proposed approach. Classification accuracy is used as a criterion to evaluate classifier performance. Results show that our proposed approach outperforms both genetic algorithms and sequential search algorithms.
關鍵字Feature selection;Particle swarm optimization;Regression;Genetic algorithms;Sequential search algorithms
語言英文(美國)
ISSN0925-9902;1573-7675
期刊性質國外
收錄於SCI;
產學合作
通訊作者L.-F. Chen
審稿制度
國別美國
公開徵稿
出版型式,電子版,紙本
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