非完备信息系统的启发式特征选择遗传算法

Heuristic Genetic Algorithm for Feature Selection in Incomplete Information Systems

为了获取非完备信息系统的相对最小特征子集,提出一种基于非完备信息系统的启发式特征选择遗传算法.本文首先构造了适应度函数,并以特征重要度为启发式信息融入特征选择;同时利用特征的相对核对种群初始化,引导染色体的进化,缩小了算法的搜索空间;且在染色体的交叉和变异过程中,对满足条件的染色体及时删除,加快算法的收敛性;实验结果验证了算法的有效性.

In this paper,in order to get the minimal relative reduction of features set,heuristic genetic algorithm for feature selection in incomplete decision table is proposed.At first,the fitness function of genetic algorithm is presented.Meanwhile,regarding feature significance as heuristic information in feature selection,and relative core feature serves as initial population to optimize chromosome,which can reduce the exploration space,what＇s more,the corresponding condition chromosomes are deleted in the crossover and mutation processes,this method can accelerate the convergence.At last,the better effect of the proposed algorithm can be tested by the experiment.