Multivariate discrete feature selection method, device, apparatu and storage medium

Abstract

The invention is applicable to the technical field of machine learning, and provides a multivariate discrete feature selection method, device, apparatus, and storage medium. The method comprises: initializing a particle swarm according to a tangent point corresponding to each feature in a target data set, obtaining the particle position of each particle, the target data set is discretized according to the particle position, the corresponding discrete data set is obtained, in accordance with discrete data set, the fitness of each particle is calculated by the fitness formula, so as to find outthe optimal position of the population of the particle swarm and the optimal position of the individual through which each particle passes, when the stop condition is met, optimal position of output population, otherwise, the particle position of each particle is updated according to the optimal position of the population and the optimal position of the individual, and the operation of data discretization and optimization is continued, so that fewer features are selected, the effect of eliminating redundant features and irrelevant features is improved, and the accuracy of classification learning algorithm is improved.

Description

technical field [0001] The invention belongs to the technical field of machine learning, and in particular relates to a multiple discrete feature selection method, device, equipment and storage medium. Background technique [0002] With the advent of the big data era, the importance of data has become increasingly prominent. Massive data promotes the development of the information society. However, with the continuous growth of data dimensions, the "dimension disaster" will be inevitable. In recent years, machine learning has been applied to various big data scenarios, such as DNA microarray analysis, image classification, text classification, etc., because these data have high data dimensions and there are some irrelevant data features in the data and redundant features, directly using the original data will affect the efficiency and performance of the learning algorithm. Therefore, in the process of machine learning, a series of preprocessing operations such as feature sel...

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Problems solved by technology

[0007] The purpose of the present invention is to provide a multivariate discrete feature selection method, device, equipment and storage medium, aiming to solve the problem of poor elimination of irrelevant features and redundant features due to the inability of the prior art to provide an effective feature selection method , the problem of inaccurate classification results

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