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:Particle Swarm Optimization for Feature Selection with Application in Obstructive Sleep Apnea Diagnosis
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
語言英文
ISSN0941-0643;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
國別英國
公開徵稿
出版型式,電子版,紙本
相關連結
Google+ 推薦功能,讓全世界都能看到您的推薦!