Feature-weighted K-nearest neighbor algorithm with SVM

被引：0

作者：

Chen, Zhen-Zhou ^{[1
,2
]}

Li, Lei ^{[1
]}

Yao, Zheng-An ^{[2
]}

机构：

[1] Inst. of Software, Sun Yat-sen Univ., Guangzhou 510275, China

[2] Sch. of Math. and Comp. Sci., Sun Yat-sen Univ., Guangzhou 510275, China

来源：

Zhongshan Daxue Xuebao/Acta Scientiarum Natralium Universitatis Sunyatseni | 2005年 / 44卷 / 01期

关键词：

Classification (of information) - Database systems - Finite automata - Learning systems - Pattern recognition - Regression analysis;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

As a nonparametric approach, K-Nearest Neighbor (K-NN) algorithm is very efficient and easily to be realized. The k-NN algorithm has been successfully used in classification, regression and pattern recognition. Two aspects, the weight of samples and the weight of features, must be paid attention to when applying K-NN to solve problems. SVM (support vector machine) to quantify the weight of features was used and FWKNN (feature-weighted K-nearest neighbor algorithm with SVM) was proposed. Experiments on artificial and natural datasets FWKNN show that, in most cases, FWKNN improve the accuracy of classification.

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页码：17 / 20

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