期刊論文
| 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 ) |