Feature selection method and device for multi-tag data

Abstract

The invention relates to a feature selection method and device for multi-label data, and belongs to the technical field of data classification. According to the method, firstly, the prior probabilityof a mark is used as the weight of the mark, the correlation between features and the mark is calculated, the features are pre-screened according to the correlation, and the correlation between the features and the mark is as large as possible while the subsequent calculation amount is reduced; the method comprises the following steps: firstly, marking samples, then dividing the same type and different types of samples by utilizing correlation link values among marking sets of the samples, finally, calculating feature weight values according to a weight updating formula, sorting the feature weight values, and selecting an optimal feature subset. Through the process, the optimal feature subset can be effectively selected, and the classification precision of the multi-label feature selectionalgorithm is improved.

Description

technical field [0001] The invention relates to a feature selection method and device for multi-label data, belonging to the technical field of data classification. Background technique [0002] In traditional supervised learning, each instance corresponds to only one class label. However, in the real world, an object often has multiple concept labels at the same time. For example, an image may have labels such as "desert", "sun", and "cactus" at the same time, so the multi-label learning problem arises at the historic moment. At present, multi-label learning has received extensive attention and has been applied in many fields such as text classification, gene function classification, image annotation, and video automatic annotation. In practical applications such as text classification, the existence of a large amount of irrelevant and redundant information in high-dimensional data greatly reduces the performance of learning algorithms. Therefore, dimensionality reduction...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a feature selection method and device for multi-label data to solve the problems of complex calculation and poor classification accuracy in the current multi-label feature selection process

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