Feature selection method based on information gain ratio

Abstract

The invention provides a feature selection method based on an information gain ratio. The method comprises the steps of sorting attributes according to the size of information gain ratio of each attribute; determining the number of selection attributes by implementing a 5-fold cross validation method for 9 times, i.e. percentage; and finally building a Naive Bayes text classifier on a selected attribute subset. According to the feature selection method based on the information gain ratio provided by the invention, the method is a mixed attribute selection method by integrating the advantages of a filtering method and a packaging method; furthermore, experimental results of a plurality of standard text classification datasets show that the classification precisions of the Naive Bayes text classifier can be improved by the feature selection method based on the information gain ratio in most situations; meanwhile, too much time expenses cannot be caused.

Description

technical field [0001] The invention relates to an attribute selection method based on information gain rate, and belongs to the technical field of artificial intelligence data mining classification. Background technique [0002] Naive Bayesian text classifier is often used to deal with text classification problems because of its simplicity and efficiency, but its attribute independence assumption affects its classification performance to some extent while making it efficient. Given a document d, the document is represented as a word vector of the form <w 1 ,w 2 ,...,w m >, Multinomial Naive Bayes (MNB), Complementary Naive Bayes (CNB) and the combined model of both (OVA) classify document d using Equations 1, 2 and 3, respectively. [0003] c ( d ) = arg max c ∈ C [ log p ...

AI Technical Summary

Problems solved by technology

The author Wang applied this method to the text classification problem and found that the simple attribute selection method seriously affected the classification accuracy, so they proposed a method based on CFS attribute weighting and achieved better classification performance

[0017] Because the attribute selection method of packaging can obtain better classification performance, but the time cost is relatively large, while the attribute selection method of filtering takes less time to select attributes, but the classification accuracy is not very good

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