基于交互信息的混合特征选择算法

针对传统的特征选择算法只专注于特征间的相关性和冗余性而没有考虑特征之间交互作用的问题,提出一种基于交互信息的混合特征选择(hybrid feature selection based on mutual information,MIHFS)算法,该算法以K-最近邻算法的分类准确率作为衡量所选特征分类性能的评价指标,有效...

Hybrid Feature Selection Algorithm Based on Mutual Information

Traditional feature selection algorithms only focus on feature correlation and feature redundancy without considering the interaction between features. This paper proposes a hybrid feature selection based on mutual information (MIHFS) algorithm. The algorithm takes the classification accuracy of K-nearest neighbor (KNN) algorithm as evaluation index to evaluate the classification performance of selected features, effectively removes redundant and irrelevant features, and retains the interactive features. In order to evaluate the performance of the proposed algorithm, the classification accuracy, the number of selected features and the stability of the algorithm are compared with seven other feature selection algorithms such as minimal redundancy maximal relevance (mRMR) and joint mutual information (JMI) in eight datasets. Experimental results show that the MIHFS algorithm has strong stability, which not only effectively reduces the dimension of feature space, but also has better classification performance than other feature selection algorithms. Finally, in combination with grey relation analysis (GRA) method-technique for order preference by similarity to ideal solution (TOPSIS) method, MIHFS algorithm is applied to the geological evaluation of the first member of Dainan Formation at Yong’an Area, Gaoyou Sag. Experimental results show that MIHFS algorithm performs an evaluation accuracy of 80% with high reliability, and this is basically consistent with actual drilling results and proves the effectiveness of MIHFS in oil and gas geological evaluation.