Adaptive Relief Feature Evaluation and Selection based on Grey Level Co-occurrence Matrix
Han Wang, Zhousheng Ma, Wenbing Fan
Available Online January 2014.
- https://doi.org/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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
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 - 2014 International Conference on Computer, Communications and Information Technology (CCIT 2014) PB - Atlantis Press SN - 1951-6851 UR - https://doi.org/10.2991/ccit-14.2014.29 DO - https://doi.org/10.2991/ccit-14.2014.29 ID - Wang2014/01 ER -