基于基因表达式编程的属性融合分类算法

bstract Traditional symbolic classification algorithms, for instance C4.5, can not deal with the relationships among attributes in dataset. For solving the problem, the contributions of this paper include: (1) Proposing a new concept called Attribute Merge, Fitness Function with Attribute Merge and a new algorithm called GEP_based Attribute Merge(GAM) on this new concept; (2) Designing a new method called Saving Best_Gene(SBG) for better convergence; (3) Proposing a new approach called Subsmall Attribute Set(SAS) for finding a sub optimal attribute set; (4) Giving extensive experiments to show that GAM is effective, on the contrary to traditional symbolic classification algorithms the average accuracy can be improved by 30%, and convergence of GAM can be improved by saving the best gene, under the same generation, the average best_fitness can be improved by 3%, and then sub-small attribute set can be discovered by SAS.