Multilabel Feature Selection Based on Fisher Score with Center Shift and Neighborhood Intuitionistic Fuzzy Entropy
The edge samples in the existing multilabel Fisher score models affect the classification effect of the algorithm.It has the available virtues of stronger expression and resolution when using neighborhood intuitive fuzzy entropy to deal with uncertain information.Therefore,this paper develops a multilabel feature selection based on the Fisher score with center shift and neighbor-hood intuitionistic fuzzy entropy.Firstly,the multilabel domain is divided into multiple sample sets according to the labels,the feature mean of the sample set is calculated as the original center point of the samples under the labels,and the distance of the furthest samples is multiplied by the distance coefficient,the edge sample set is removed,and then a new effective sample set is defined.The score of each feature under the labels is calculated after center migration processing and the feature score of the label set.Then,a multilabel Fisher score model is established based on center migration to preprocess multilabel data.Secondly,the multilabel classification interval is introduced as the adaptive fuzzy neighborhood radius parameter,the fuzzy neighborhood simi-larity relation and fuzzy neighborhood particle are defined,and the upper and lower approximate sets of the multilabel fuzzy neighborhood rough sets are constructed.On this basis,the rough intuitive membership function and non-membership function of multilabel neighborhood are proposed,and the multilabel neighborhood intuitionistic fuzzy entropy is defined.Finally,the formu-las for calculating the external and internal significance of features are obtained,and a multilabel feature selection algorithm based on neighborhood intuitive fuzzy entropy is designed to screen the optimal feature subset.Under the multilabel K-nearest neighbor classifier,experimental results on nine multilabel datasets show that the optimal subset selected by the proposed algorithm has great classification effect.
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