Method and device for selecting characteristics based on neural network sensitivity

Abstract

The invention discloses a method and device for selecting characteristics based on neural network sensitivity, and belongs to the field of machine learning in intelligent science and technologies. The method and device are based on the sensitivity, the characteristics high in sensitivity are selected to search for the characteristics with output greatly changed when disturbance happens to the characteristics, and the characteristics are important to a training classifier. The method and device can effectively select the sample characteristics important to the training classifier, and therefore the performance of the classifier is improved.

Description

technical field [0001] The present invention relates to a learning sample feature selection method and its device during neural network design, in particular to a learning sample feature selection method and device based on feature selection that can effectively improve the neural network classification efficiency, and belongs to machine learning in intelligent science and technology field. Background technique [0002] When designing some kinds of neural network classifiers, due to the high dimensionality of feature vectors, it often leads to a huge network structure. Huge network will lead to training difficulties, need more training samples, long training time and other disadvantages. In practical applications, it is often desirable to use samples with lower dimensions to construct a classifier with better generalization performance. [0003] Feature selection technology is a technique that converts samples with higher dimensions into samples with lower dimensions throu...

AI Technical Summary

Problems solved by technology

Encapsulation uses the training accuracy of the follow-up learning algorithm to evaluate the feature subset, with small deviation and large amount of calculation, which is not suitable for large data sets

Images