Text Classification Method with Combination of Fuzzy Relation and Feature Distribution Variance

Abstract

To accurately express the fuzzy relation between word features and texts, and fuzzy relation between word features and categories respectively. A text classification method is proposed based on Fuzzy Relation and Feature Distribution Variance (FRFDV). This method firstly performs feature reduction and category feature word extraction according to the distribution of features in inter-category and intra-category. Then the method defines the word feature set, test text set and category set as fuzzy sets. Next, each text and category are represented respectively by defining the membership function of the word feature set to the test text set and the category set. When using word feature sets to represent categories, pay attention to the membership degree of features to categories and their distribution between categories; when using feature sets to represent test texts, give categorical feature words and non-categorical feature words with different weights. Finally, the fuzzy set correlation formula is used to calculate the correlation between the text and each category, and the category with the largest correlation is the category of the text. Comparing with the XGBOOST [Fang, 2020, Gong and Wang, 2018] algorithm and SVM algorithm, it is proved that the text classification method based on FRFDV is feasible. The accuracy of the results is higher by 2 % and 4 % respectively.