【摘要】 MIFS算法及其改进算法对医疗诊断数据集进行特征选择时，秉承“最大相关最小冗余”的思想，关注特征与类别的相关信息和特征之间的冗余信息，没有考虑到特征之间的交互信息。考虑到医疗诊断指标之间的交互，本文提出一种基于特征交互的MIFS算法(Feature Interaction Based MIFS Algorithm, MIFS-FI),在实现“最大相关”的同时，最大程度地去除冗余特征，保留交互特征，还有效地解决了MIFS算法中参数不确定以及相关项与冗余项不可比的问题。将MIFS-FI算法和其他7种基于互信息的特征选择方法应用于14个医疗诊断数据集进行对比实验，结果表明MIFS-FI算法在分类准确率、召回率和F1值三方面优于其他7种特征选择方法，提高了分类精度。更多还原

【Abstract】 MIFS algorithm and its improved algorithms adhere to the idea of "maximum correlation and minimum redundancy" to select features in medical diagnostic data sets, which pay attention to the relevant information and the redundant information, but do not consider the interaction information. In order to emphasize the role of Interaction information, Feature Interaction Based MIFS Algorithm（MIFS-FI） is proposed. MIFS-FI algorithm achieve "maximum correlation", and the redundant features are almost removed and the interactive features are nearly retained. Secondly, it effectively solves the problems of parameter uncertainty and correlation-redundancy incomparable in MIFS algorithm. Finally, the MIFS-FI algorithm and seven other feature selection methods based on mutual information are compared to 14 medical diagnosis datasets, and the results show that the MIFS-FI algorithm outperforms the others in terms of classification accuracy, recall,F₁score and classification accuracy.更多还原