A Feature Selection Method Based on Improved Genetic Algorithm
Kui Liang, Wei Dai, Rui Du · 2020
Fault diagnosis is an important research topic in the field of reliability, but with the process of industrialization, the amount of data becomes huge, its essential to feature selection before data analysis. This paper proposes a feature selection method based on improved genetic algorithm (GA). First sorts the features by feature evaluation criteria to exclude irrelevant features. In this way, the dimension of the features is reduced, and then based on the simulated annealing algorithm (SA) to solve the problem that the GA jumps out of the local optimal solution. then, using the SVM for fault classification verified the effectiveness of the method based on the Tennessee Eastman (TE) data set. The results show that this method has better applicability than the traditional genetic algorithm for feature selection.