A new particle swarm feature selection method for classification | |
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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 | en_US |
ISSN | 0925-9902 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 | 是 |
國別 | USA |
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/987654321/107036 ) |