Research of multi-sided multi-granular neural network ensemble optimization method.

According to the thought \"divide and conquer\" that human perceives complicated things from multi-side and multi-view and balances the final decision, this paper puts forward the multi-sided multi-granular neural network ensemble optimization method based on feature selection, which divides attribute granularity of dataset from multi-side, and structures multi-granular individual neural networks using different attribute granularity and the corresponding subsets. In this way, we can gain multi-granular individual neural networks with greater diversity, and get better performance of neural network ensemble(NNE). Firstly, use feature selection method to calculate the importance of each attribute, according to the average weight to choose some attributes whose average weight is greater than a certain threshold, to form an attribute granularity and the corresponding sample subset, thus to construct an individual neural network. If samples are not properly identified, this attribute granularity is weak for the generalization ability of the sample. Secondly, again calculate the importance of the attributes of samples not properly identified, choose the attributes that can generalize the corresponding samples better, and add to the last attribute granularity to form a new attribute granularity, and at the same time random choose two-thirds of sample subset to construct an individual neural network. In turn, one can get a series of attribute granularities and the corresponding sample subsets and a series of multi-granular individual neural networks. These attribute granularities and the corresponding sample subsets constructed from multi-side and multi-view with greater diversity can construct multi-granular individual neural networks with greater diversity. This method not only reduces the dimension of the dataset, but also makes the attribute granularity to identify the corresponding sample as large as possible. Finally, by calculating the diversity of each of the two individual neural networks, optimal selects some individual neural networks with greater diversity to ensemble. The simulation experiments show that our proposed method here, multi-side multi-granular neural network ensemble optimization method, can gain better performance.