教師資料查詢 | 類別: 會議論文 | 教師: 翁慶昌WONG CHING-CHANG (瀏覽個人網頁)

標題: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
語言英文
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
國別中華民國
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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