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 en
ISSN 1088-467X 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 是
國別 NLD
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/107027 )