期刊論文
學年 | 100 |
---|---|
學期 | 1 |
出版(發表)日期 | 2011-12-31 |
作品名稱 | Particle Swarm Optimization for Feature Selection with Application in Obstructive Sleep Apnea Diagnosis |
作品名稱(其他語言) | |
著者 | L.-F. Chen; C.-T. Su; K.-H. Chen; P.-C. Wang |
單位 | |
出版者 | |
著錄名稱、卷期、頁數 | Neural Computing and Applications 21(8), pp.2087-2096 |
摘要 | Feature selection is a preprocessing step of data mining, in which a subset of relevant features is selected for building models. Searching for an optimal feature subset from a high-dimensional feature space is an NP-complete problem; hence, traditional optimization algorithms are inefficient in solving large-scale feature selection problems. Therefore, meta-heuristic algorithms are extensively adopted to effectively address feature selection problems. In this paper, we propose an analytical approach by integrating particle swarm optimization (PSO) and the 1-NN method. The data sets collected from UCI machine learning databases were used to evaluate the effectiveness of the proposed approach. Implementation results show that the classification accuracy of the proposed approach is significantly better than those of BPNN, LR, SVM, and C4.5. Furthermore, the proposed approach was applied to an actual case on the diagnosis of obstructive sleep apnea (OSA). After implementation, we conclude that our proposed method can help identify important factors and provide a feasible model for diagnosing medical disease. |
關鍵字 | Feature selection;Particle swarm optimization;Obstructive sleep apnea;Genetic algorithm |
語言 | en |
ISSN | 0941-0643 1433-3058 |
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 | GBR |
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/987654321/107025 ) |