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 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 )