关键词: 覆盖算法, 特征选择, 多侧面递进

Abstract: The multi-side covering algorithm is designed guided by the idea of divide-and-conquer to the mass high-dimensional data. According to the sum of the absolute value of the component deviation, subsets of attributes were selected to construct respective covering domains for different parts of training samples, thus reducing the complexity of learning. But the selection of initial attribute set should be acquired by experience or experiments. In order to reduce the subjectivity with the selection of initial attribute set and the complexity with the regulation of attribute set, the relief feature selection approach was used to ensure the optimal feature subset that can be appropriate for different data sets, build a hierarchical overlay network, and experiment on the actual data set. The experimental results show that this algorithm is provided with higher precision and efficiency. Therefore, the algorithm can effectively achieve the classification of the complex issues.

Key words: covering algorithm, feature selection, multi-side increase by degree