Adaptive Relief Feature Evaluation and Selection based on Grey Level Co-occurrence Matrix
Han Wang, Zhousheng Ma, Wenbing Fan · 2014
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 cooccurrence 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.