A Hybrid Prototype Construction and Feature Selection Method with Parameter Optimization for Support Vector Machine
學年 97
學期 1
發表日期 2008-11-13
作品名稱 A Hybrid Prototype Construction and Feature Selection Method with Parameter Optimization for Support Vector Machine
作品名稱（其他語言）
著者 Wong, Ching-Chang; Leu, Chun-Liang
作品所屬單位 淡江大學電機工程學系
出版者
會議名稱 The 2008 International Computer Symposium(ICS 2008)
會議地點 臺北縣, 臺灣
摘要 In this paper, an order-independent algorithm for data reduction, called the Dynamic Condensed Nearest Neighbor (DCNN) rule, is proposed to adaptively construct prototypes in training dataset and to reduce the over-fitting affect with superfluous instances for the Support Vector Machine (SVM). Furthermore, a hybrid model based on the genetic algorithm is proposed to optimize the prototype construction, feature selection, and the SVM kernel parameters setting simultaneously. Several UCI benchmark datasets are considered to compare the proposed GA-DCNN-SVM approach with the GA-based previously published method. The experimental results show that the proposed hybrid model outperforms the existing method and improves the classification accuracy for SVM.
關鍵字 Dynamic condensed nearest neighbor;Prototype construction;Feature selection;Genetic algorithm;Support vector machine
語言 en
收錄於
會議性質 國際
校內研討會地點 淡水校園
研討會時間 20081113~20081113
通訊作者
國別 TWN
公開徵稿 Y
出版型式 紙本
出處 Proceedings of the 2008 International Computer Symposium (ICS 2008)，6頁
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/95955 )

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