Feature selection method and traditional Chinese medicine main-symptom selection method based on feature groups

Abstract

The invention discloses a feature selection method and a traditional Chinese medicine main-symptom selection method based on feature groups. The feature selection method based on the feature groups comprises the following steps: 1, screening an original feature set; 2, utilizing a feature clustering algorithm to cluster the screened feature set to obtain the corresponding feature groups; 3, introducing a latent variable to each feature group to obtain a corresponding latent class model (LCM), and calculating correlation between the latent variables and labels; 4, sorting the feature groups sequentially with decreasing according to the correlation between the latent variables and the labels; 5, adding the sorted feature groups into selected feature subsets in turn, and establishing a Bayesian network with the latent variables; and 6, calculating classification accuracy rates of the Bayesian network, then obtaining a curve of the numbers of the added feature groups and the classification accuracy rates, and obtaining the corresponding optimal feature subset through judging the convergence of the curve or the highest accuracy rate. According to the method, the feature groups are used as selected targets, and the feature groups composed of a plurality of features have better representation capacity for original data.

Description

technical field [0001] The invention relates to the field of pattern recognition and machine learning research, and the feature relates to a feature selection method based on feature groups. Background technique [0002] The main purpose of Feature Selection is to select the optimal feature subset that meets the specified evaluation criteria from the original features, so that the classification model or regression model constructed by the selected optimal feature subset achieves better performance than before feature selection. Better performance, through feature selection, not only improves the generalization ability of the model, so that the model can be better explained to improve computational efficiency, but also reduces the occurrence of dimensionality disaster. [0003] In the traditional feature selection algorithm, the selection of features is mainly for a single feature, considering the correlation between each feature and the label, and choosing each feature with...

AI Technical Summary

Problems solved by technology

There may be dozens or even hundreds of possible symptoms corresponding to a certain syndrome type in TCM syndrome differentiation, which will bring difficulties to the research of TCM syndrome differentiation. The purpose of the invention is to provide a feature-based The selection method of the main symptoms of traditional Chinese medicine, embodying the syndrome elements in the syndrome differentiation model of traditional Chinese medicine can make the syndrome differentiation model of traditional Chinese medicine more consistent with the theory of traditional Chinese medicine

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