A Hybrid Prototype Construction and Feature Selection Method with Parameter Optimization for Support Vector Machine

淡江大學機構典藏 > 工學院 > 電機工程學系暨研究所 > 會議論文 > Item 987654321/95955

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题名:	A Hybrid Prototype Construction and Feature Selection Method with Parameter Optimization for Support Vector Machine
作者:	Wong, Ching-Chang;Leu, Chun-Liang
贡献者:	淡江大學電機工程學系
关键词:	Dynamic condensed nearest neighbor;Prototype construction;Feature selection;Genetic algorithm;Support vector machine
日期:	2008-11
上传时间:	2014-02-13 11:26:25 (UTC+8)
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.
關聯:	Proceedings of the 2008 International Computer Symposium (ICS 2008)，6頁
显示于类别:	[電機工程學系暨研究所] 會議論文

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A Hybrid Prototype Construction and Feature Selection Method with Parameter Optimization for Support Vector Machine_英文摘要.docx	21Kb	Microsoft Word	151	检视/开启

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