关键词: 特征选择, 最大信息系数, 马尔科夫毯代表集, 次最优特征子集, 适应度函数, 粒子群算法

Abstract:

For the problem of high dimensional data classification that does not satisfy the faithful distribution, a new Markov blanket feature selection algorithm based particle swarm optimization is proposed. By effectively extracting relevant features and eliminating redundant features, it can produce better classification results. In the feature preprocessing stage, this algorithm analyzes the correlation and redundancy of features by using the maximum information coefficient measurement standard to obtain the Markov blanket representative set and sub-optimal feature subset of class attributes. In the search and evaluation stage, a new fitness function is used to select the optimal feature subset by particle swarm optimization. The model is used to predict the testing set. Experimental results show that the algorithm has certain advantages on twelve datasets.

Key words: feature selection, maximal information coefficient, Markov blanket representative set, suboptimal feature subset, fitness function, particle swarm optimization