Adaptive Relief Feature Evaluation and Selection based on Grey Level Co-occurrence Matrix
- 10.2991/ccit-14.2014.29How to use a DOI?
- adaptive Relief algorithm, feature selection, image recognition, GLCM.
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.
- © 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/).
Cite this article
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 -