关键词: 特征选择, 量子进化算法, 遗传算法, 特征子集, 特征维度

Abstract: Feature selection is widely studied as a data preprocessing technology because it has the NP difficulty and an effective solution has not been found. Since the genetic algorithm which applies most in feature selection at present has the limit in evolutionism, Quantum Evolutionary Algorithm（QEA） is applied to feature selection, and a feature selection algorithm based on improved QEA is put forward. Firstly, QEA is improved in order to enhance its optimization performance and increase its population diversity. Then, the fitness function is constructed according to the evaluation criteria of feature subset based on the Fisher ratio and the feature dimensionality. Lastly, a feature selection algorithm is designed in the light of the steps to solve the optimization problem by QEA. Use data in UCI data base to make comparison validation experiments to 3 algorithms, such as identify important features, improve learning algorithm performance, and feature selection efficiency. The experimental results show that the algorithm can identify important features, and with the going up of the data set feature dimension, the performance of feature selection is gradually better than contrast algorithm; when it comes to high dimensional data set, feature selection efficiency is obviously better than the contrast algorithm.

Key words: feature selection, Quantum Evolutionary Algorithm（QEA）, Genetic Algorithm, feature subset, feature dimension