Input Space Partitioning for Neural Network Learning
Shujuan Guo, Sheng-Uei Guan, Weifan Li, Ka Lok Man, Fei Liu, Alex Kai Qin · International Journal of Applied Evolutionary Computation · 2013
To improve the learning performance of neural network (NN), this paper introduces an input attribute grouping based NN ensemble method. All of the input attributes are partitioned into exclusive groups according to the degree of inter-attribute promotion or correlation that quantifies the supportive interactions between attributes. After partitioning, multiple NNs are trained by taking each group of attributes as their respective inputs. The final classification result is obtained by integrating the results from each NN. Experimental results on several UCI datasets demonstrate the effectiveness of the proposed method.