A Novel Feature Selection and Extraction Technique for Classification

TL;DR

This paper introduces Class Dependent Features (CDFs), a versatile technique for feature selection and extraction that enhances classification accuracy while managing computational costs, demonstrated on digit recognition and text categorization.

Contribution

The paper proposes a new feature selection and extraction method called CDFs, applicable across different domains to improve classification performance and efficiency.

Findings

Improved classification accuracy with CDFs

Reduced computational expense in high-dimensional data

Effective application to both image and text data

Abstract

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.