Hybrid Model Initializing the Genetic Population with Multiple Filters for Feature Selection

Feature selection method has become the focus of the research in the area of high-dimensional engineering data processing,especially pattern recognition.In this paper,a hybrid feature selection model is presented to select the most significant features from all potentially relevant features.The model combines a filter with a wrapper.In the filter,four variable ranking methods are used to pre-rank the candidate features,and then an initial GA population is produced based on the degree of significance of the re-rank features.In the wrapper,GA algorithm is utilized to search the feature subsets evaluated by the classification error rate of neural network classifier,which can help find the most feature subset.Tests to some datasets demonstrate that the presented model not only can reduce dimensionality of feature subset,but also can improve the accuracy and efficiency of classification.