关键词: 特征选择, filter方法, 贝叶斯和谐度, 降维

Abstract: Feature selection is a key technique for the dimension reduction in high-dimensional data mining. Traditional data dimension reduction techniques such as PCA which just converts the expression of the dataset cannot reflect the relevant degree within the dataset. In order to select the optimal combination of features, an information measure is commonly used in the process of feature selection by the computation of the uncertain degree of the dataset. The choice of metric function in this method determines the feature subsets. Compared with the method using information entropy, a feature selection algorithm based on Bayesian harmony measure is presented in this paper. The increase in the harmony degree is used to measure the relevant degree within the selected features. The Bayesian harmony measure is introduced from the Bayesian Ying-Yang harmony learning theory, which can estimate the joint distribution of the input space. The similarity between two classes can be estimated by the change of Bayesian harmony. At the same time it can obtain the low redundancy feature combination by computing the harmony degree amplification in the entire system. Compared with traditional methods such as ReliefF and FCBF, the method has better performance in the case of selecting the same number of features.

Key words: feature selection, filter method, Bayesian harmony measure, dimension reduction