关键词: 和声搜索, 特征选择, 启发式算法, 维数约减, 大数据

Abstract: Feature selection technology can effectively solve the curse of dimensionality problem, and many search strategies have been applied to feature selection problems. Aiming at the low search ability of harmony feature selection algorithm, an improved Harmony Search Feature Selection algorithm based on self-adaptation Global Pitch Adjusting（HSFS-GPA） is proposed to enhance the exploitation ability of solution space. The distance between feature sets is introduced into the feature selection problem. In the algorithm search process, a new harmony is generated by combining with the global information. The distance between the candidate harmony and the current optimal harmony or the worst harmony is changed. At the same time, competition selection mechanism is established to improve solution precision and enhance the ability of escaping local optima, the information of the worst harmony in harmony memory is updated at each iteration. HSFS-GPA is compared with the original harmony feature selection algorithm, particle swarm optimization algorithm and genetic algorithm. The size of the feature subset selected by HSFS-GPA is reduced by 15% than that of the original harmony search algorithm, and the average of the subset evaluation is increased to 0.98. The experimental result shows that HSFS-GPA can search for a better feature subset under the same condition.

Key words: harmony search, feature selection, meta heuristics, dimension reduction, big data