A Kind of FAST Feature Selection Algorithm Considering Feature Interaction
Abstract: Interacting features are those appear to be irrelevant or weakly relevant with the class individually, but it may highly correlate to the class when it combined. Feature interaction is almost everywhere, but discovering feature interaction is a challenging task in feature selection. The purpose of this paper is to improve the FAST feature selection algorithm based on cluster by considering feature interaction. Firstly, deleted the irrelevant feature removal section, then brought in an interaction weight factor, so that we can retain interacted features when removed the irrelevant and redundant ones. In order to do the comparison between this two algorithms, we selected 16 public data sets which cover 5 different domains on the empirical analysis, and used 4 types of classifier to evaluate the results, namely, C5.0, Bayes Net, Neural Net and Logistic. Finally, we compared these two algorithms according to the number of selected features, running time of algorithm and the accuracy of classifier. The experimental result showed that it has little difference on the number of selected features, and sometimes IWFAST can produce smaller subsets of features. Meanwhile, IWFAST can improve the accuracy of the classifier, especially for the high- dimensional data set, or especially for the Game and Life area. The defect is that the running time of IWFAST is long, but is acceptable computational complexity.
文章引用: 陆碧云 , 张 磊 (2017) 考虑了特征协同作用的FAST特征选择算法的改进。 数据挖掘， 7， 51-63. doi: 10.12677/HJDM.2017.72006
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