Classification rule mining using feature selection and genetic algorithm

Classification rule mining has been a very active research topic in data mining and machine learning communities. To effectively cope with this problem, a novel classification rule mining algorithm is proposed by the combination of neighborhood preserving embedding (NPE) and genetic algorithm (GA) in this paper. Experimental results on the UCI data set repository demonstrate that the proposed algorithm performs much better than other well-known classification rule mining algorithms.