Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4948059 | Neurocomputing | 2017 | 25 Pages |
Abstract
Feature selection as one of the key problems of data preprocessing is a hot research topic in pattern recognition, machine learning, and data mining. Evaluating the relevance between features based on information theory is a popular and effective method. However, very little research pays attention to the distinguishing ability of feature, i.e., the degree of a feature distinguishes a given sample with other samples. In this paper, we propose a new feature selection method based on the distinguishing ability of feature. First, we define the concept of maximum-nearest-neighbor, and use this concept to discriminate the nearest neighbors of samples. Then, we present a new measure method for evaluating the quality of feature. Finally, the proposed algorithm is tested on benchmark datasets. Experimental results show that the proposed algorithm can effectively select a discriminative feature subset, and performs as well as or better than other popular feature selection algorithms.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Jinghua Liu, Yaojin Lin, Menglei Lin, Shunxiang Wu, Jia Zhang,