一种融合蚁群算法和随机森林的特征选择方法

Abstract

Abstract: In the face of massive high-dimensional data,eliminating redundant features for feature selection has become one of the important issues faced by information and science and technology today.Traditional feature selection methods are not suitable for searching the whole feature space,and their performance and accuracy are low.In this paper,a me-thod of feature selection based on ant colony optimization and random forest was proposed.This method takes the importance score of random forest as the heuristic factor of ant colony optimization,uses ant colony optimization to search intelligently,and uses the result of feature selection as the evaluation index to feedback the pheromone of ant colony in real time.Experiments show that this feature selection method can effectively reduce the number of features in data sets and improve the accuracy of data classification compared with traditional feature selection methods.

Key words: Ant colony optimization, Feature selection, Random forest

CLC Number:

TP391

LI Guang-hua, LI Jun-qing, ZHANG Liang, XIN Yan-sen, DENG Hua-wei. Feature Selection Method Based on Ant Colony Optimization and Random Forest[J].Computer Science, 2019, 46(11A): 212-215.

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Feature Selection Method Based on Ant Colony Optimization and Random Forest

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