Adaptive Relief Feature Evaluation and Selection based on Grey Level Co-occurrence Matrix

DOI

10.2991/ccit-14.2014.29How to use a DOI?

Keywords

adaptive Relief algorithm, feature selection, image recognition, GLCM.

Abstract

In image recognition, how to select informative features from the feature space is a very significant task. Relief algorithm is considered as one of the most successful methods for evaluating the quality of features. In this paper, it firstly provides a valid proof which demonstrates a blind selection problem in the previous Relief algorithm. And then this paper proposes an adaptive Relief (A-Relief) algorithm to alleviate the deficiencies of Relief by dividing the instance set adaptively. Lastly, it uses grey level co-occurrence matrix (GLCM) to extract text features and applies A-Relief algorithm to classify these features. The experimental results illustrate A-Relief algorithm proposed in this paper can improve the accuracy of the classification effectively and solve the blind selection problem.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Han Wang
AU  - Zhousheng Ma
AU  - Wenbing Fan
PY  - 2014/01
DA  - 2014/01
TI  - Adaptive Relief Feature Evaluation and Selection based on Grey Level Co-occurrence Matrix
BT  - Proceedings of the 2014 International Conference on Computer, Communications and Information Technology
PB  - Atlantis Press
SP  - 106
EP  - 109
SN  - 1951-6851
UR  - https://doi.org/10.2991/ccit-14.2014.29
DO  - 10.2991/ccit-14.2014.29
ID  - Wang2014/01
ER  -