High-dimensional data feature selection based on relevance analysis and GA

Feature selection is one of the important problems in the pattern recognition and data mining areas.For high-dimensional data feature selection not only can improve the accuracy and efficiency of classification,but also can discover informative feature subset.The new feature selection method combining filter and wrapper models was proposed,which first filters featured by feature relevance analysis,and realized the near optimal feature subset search on the compact feature subset by genetic algorithm;and the feature subset was evaluated by the classification inaccuracy of the perceptron model.The experiments show that the proposed algorithm can find the feature subsets with good linear separability,which results in the low-dimensional data and the good classification accuracy.