Feature selection method for pattern recognition of small sample data

Abstract

The invention relates to a feature selection method for pattern recognition of small sample data, constructing an SVM classification face model according to a training sample set at first and ensuring frontier point of SVM classification face; selecting reasonable heterogeneous frontier point pairs and calculating classification face points of each reasonable heterogeneous frontier point pair; calculating importance metric of each characteristic from a characteristic importance measurement model based on classification face points of all classification face models; carrying out characteristic ordering and sorting based on the size of the calculated characteristic importance metric. The method has characteristics of accuracy and rapidity of importance measurement and can satisfy practical needs for characteristic sorting and ordering designed by a small sample classifier. The method provides a new effective technology for characteristic sorting and ordering in a pattern recognition field.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition and relates to a feature selection method for pattern recognition of small sample data. Background technique [0002] The pattern recognition system is mainly composed of four parts: data acquisition, preprocessing, feature selection and extraction, classification decision, such as figure 1 shown. The feature selection and extraction part is to select and extract the features that best reflect the nature of the classification based on the original data. Feature selection is a key issue in pattern recognition. Because it is often not easy to find those important features in many practical problems, this complicates the task of feature selection and extraction and becomes one of the most difficult tasks in constructing a pattern recognition system. This problem has been paid more and more attention. [0003] The feature importance method is the core of feature selection and ranking. ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a feature selection method for pattern recognition of small sample data, to solve the problem that existing methods are not suitable for feature selection processing of small sample data

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