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:An Improved Particle Swarm Optimization for Feature Selection
學年101
學期1
出版(發表)日期2012/12/01
作品名稱An Improved Particle Swarm Optimization for Feature Selection
作品名稱(其他語言)
著者L.-F. Chen; C.-T. Su; K.-H. Chen
單位
出版者
著錄名稱、卷期、頁數Intelligent Data Analysis 16(2), pp.167-182
摘要Searching for an optimal feature subset in 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 have been extensively adopted to solve the feature selection problem efficiently. This study proposes an improved particle swarm optimization (IPSO) algorithm using the opposite sign test (OST). The test increases population diversity in the PSO mechanism, and avoids local optimal trapping by improving the jump ability of flying particles. Data sets collected from UCI machine learning databases are used to evaluate the effectiveness of the proposed approach. Classification accuracy is employed as a criterion to evaluate classifier performance. Results show that the proposed approach outperforms both genetic algorithms and sequential search algorithms.
關鍵字Feature selection;particle swarm optimization;genetic algorithms;sequential search algorithms
語言英文
ISSN1088-467X;1571-4128
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
國別荷蘭
公開徵稿
出版型式,電子版,紙本
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