A Novel Feature Selection and Extraction Technique for Classification

被引：0

作者：

Goel, Kratarch ^{[1
]}

Vohra, Raunaq ^{[2
]}

Bakshi, Ainesh ^{[3
]}

机构：

[1] BITS Pilani KK Birla, Dept Comp Sci, Goa Campus, Zuarinagar, Goa, India

[2] BITS Pilani KK Birla, Dept Math, Zuarinagar, Goa, India

[3] Rutgers State Univ, Dept Comp Sci, New Brunswick, NJ 08903 USA

来源：

2014 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC) | 2014年

关键词：

MNIST; USPS; WebKB; Reuters-21578;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a versatile technique for the purpose of feature selection and extraction - Class Dependent Features (CDFs). We use CDFs to improve the accuracy of classification and at the same time control computational expense by tackling the curse of dimensionality. In order to demonstrate the generality of this technique, it is applied to handwritten digit recognition and text categorization.

引用

页码：4033 / 4034

页数：2

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