Dynamic feature selection method for mode classification

Abstract

The invention discloses a dynamic feature selection method which is used for mode classification. The method is carried out according to the following steps: a requirement is sent out to a knowledge base after a sample is preprocessed by a preprocessing module, if the requirement is a classifying requirement, the sample is regularized by an optimal feature combination that can be obtained by the preprocessing module from the knowledge base and then is handed into a classifier for classification; if the requirement is a feature selection requirement, a part of samples are output to the knowledge base by the preprocessing module and is combined with a part of samples of the knowledge base to carry out the combination, a part of samples are output from the combined samples to enter into a feature selection module, the parameter and the ratio coefficient that need to be dynamically adjusted by the feature selection module and the classifier are output from the knowledge base, the feature selection module and the classifier are instructed to carry out the feature selection and the relevant parameter is fed back to the knowledge base for carrying out the knowledge updating after the selection is finished. The method can dynamically select the optimal feature combination from the mode samples that are continuously changed, thereby being more accordant with the actual situation and satisfying the requirements of high-precision classification.

Description

technical field [0001] The invention relates to the technical field of pattern classification, in particular to a feature selection method for feature selection of dynamically changing pattern samples for pattern classification. Background technique [0002] Pattern classification is currently widely used in many fields such as electric power, finance, commerce, military affairs, medicine and health, and its processing process consists of sample preprocessing, feature extraction, feature selection, and classification. Among them, feature selection is an important process in the pattern classification system. In actual pattern classification, pattern samples to be classified often contain a large number of features. Feature selection can delete features that are irrelevant or less useful for classification from these large number of features, and select features that are very useful for classification. It can effectively improve the efficiency and classification accuracy of ...

AI Technical Summary

Problems solved by technology

Therefore, when the static feature selection method is used to process dynamic pattern samples, it often occurs that the optimal feature combination that meets certain classification requirements for the training sample is used for other samples, and the classification accuracy rate is significantly reduced or unstable.

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