High-dimensional data classification method based on two-stage mixed feature selection

Abstract

The invention discloses a high-dimensional data classification method based on two-stage mixed feature selection. The method comprises the following steps: obtaining a processed data set; preprocessing the processed data set based on a maximum information coefficient (MIC) method to obtain an MIC matrix; obtaining a selected feature subset; performing fine search on the selected feature subset by using an improved PSO algorithm to obtain an optimal feature subset; updating features in the processed data set obtained in the step S1 according to the optimal feature subsets, establishing a training set and a test set for ten-fold cross validation according to the updated data set, and sequentially inputting the training set and the test set into a KNN classifier of which K is equal to 1 to obtain the classification accuracy of the corresponding ten optimal feature subsets; and taking the average value of the classification accuracy rates of the ten optimal feature subsets as the accuracy rate of the optimal feature subsets.

Description

technical field [0001] The present invention relates to technical fields such as reinforcement learning, feature selection, pattern recognition, machine learning, etc., and specifically relates to a high-dimensional data classification method based on two-stage mixed feature selection. Background technique [0002] With the rapid development of science and technology, more and more data are collected in machine learning tasks. There are a large number of irrelevant and redundant features in these data, which will reduce the prediction accuracy of the model and increase the computational complexity. Therefore, how to filter out the features most relevant to the task to be solved has become an urgent problem in machine learning and pattern recognition. As an effective tool for reducing the feature dimension, feature selection can eliminate useless features in the original data according to a given evaluation standard, save computing costs and improve prediction accuracy. In ...

AI Technical Summary

Problems solved by technology

Although it can achieve satisfactory results, there are still some problems, such as premature convergence, easy to fall into local optimum, and easy overfitting when dealing with high-dimensional data.

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