Supervised feature selection by self-paced learning regression

Abstract

Abstract Feature selection is an effective method to reduce the feature dimension and improve the efficiency of data mining algorithms, it can be used to solve the ”curse of dimensionality” problem. Most existing feature selection methods do not adequately consider the effects of noise and outliers, thereby reducing the performance of regression. The paper proposed a new feature selection model and supervised learning method, using self-paced regularizer (SP-regularizer) terms and l2, 1-norm to constrain the model. Specifically, the feature selection model is trained firstly using the most high-confidence samples base on the self-paced learning theory, and then adds the more high-confidence training samples in the remaining samples to increase the generalization ability of the initial feature selection model until the generalization ability of the feature selection model is not improved or all training samples are used up. Then use l2, 1-norm remove redundant feature to improve algorithm efficiency. Experimental results on nine datasets show that our proposed method is superior to the comparison algorithms.