关键词: 多目标人工蜂群算法, 特征选择, Knee Points, 分类算法

Abstract: In classification problem, there are a large number of redundant and irrelevant features in datasets which  not only can??t increase the accuracy of classification, but also reduce the speed of the classification algorithm. Feature selection can solve these problems by maximizing the classification accuracy and minimizing the number of features, and it can be regarded as a multi-objective optimization problem because these are two contradictory objects. In order to enhance the efficiency of feature selection, an improved multi-objective artificial bee colony algorithm based on Knee Points for feature selection is proposed in this paper. A method for fast recognizing Knee Points is designed to improve the employed and onlooker bee phase. Experiment results on feature selection on 11 UCI datasets with 3 other traditional multi-objective algorithms show that the algorithm proposed in this paper has significant effect in reducing the number of classification and increasing the accuracy of classification.

Key words: multi-objective artificial bee colony algorithm, feature selection, Knee Points, classification algorithm