Feature Selection for Image Classification Based on a New Ranking Criterion

Xuan Zhou,Jiajun Wang

doi:10.4236/jcc.2015.33013

Abstract

In this paper, a feature selection method combining the reliefF and SVM-RFE algorithm is proposed. This algorithm integrates the weight vector from the reliefF into SVM-RFE method. In this method, the reliefF filters out many noisy features in the first stage. Then the new ranking criterion based on SVM-RFE method is applied to obtain the final feature subset. The SVM classifier is used to evaluate the final image classification accuracy. Experimental results show that our proposed relief- SVM-RFE algorithm can achieve significant improvements for feature selection in image classification.

Highlights

Feature selection plays a key role in many pattern recognition problems such as image classification [1] [2]
ReliefF is first used to select 300 features as the candidate subset from the all feature data, support vector machine (SVM) recursive feature elimination (SVM-RFE) is utilized to choose the final subset according to our new ranking criterion
Comprehensive experiments are performed on dataset-Caltech to compare our new proposed selection algorithm with the reliefF and SVM-RFE algorithms

Summary

Introduction

Feature selection plays a key role in many pattern recognition problems such as image classification [1] [2]. The reliefF algorithm is a typical example of this type which can effectively provide quality of each feature in problems with dependencies among the feature space [3]. The SVM recursive feature elimination (SVM-RFE) method is a typical wrapper type selector, where the support vector machine (SVM) [5] is used as a classifier This method was firstly employed in gene selection [6], where the ranking criterion for different features is determined as the magnitude of the weights of the trained SVM classifier. A new hybrid model is proposed by combing reliefF algorithm and SVM-RFE method. SVM-RFE method is applied to directly estimate the quality of each feature resulted from the reliefF algorithm according to our proposed evaluation criterion. The experimental results demonstrate that the proposed feature selection algorithm outperforms the other two methods

Feature Extraction

Feature Selection

SVM-RFE Algorithm for Feature Selection

An Improved Algorithm by Combing reliefF and SVM-RFE

Experimental Results and Discussion

Conclusion and Feature Work