基于二次选择匹配的神经网络集成构造方法研究

研究12种基于二次选择匹配的选择性神经网络二次集成构造方法.分析了两次集成中所用选择方法的匹配关系及第1级结构中选择性神经网络集成个体的个数对神经网络集成效果的影响.仿真结果表明,通过采用二次选择匹配以及一定的集成个数可以保证个体具有较高的精度、差异度,并减少过拟合对集成结果的影响,提高神经网络的集成精度.

Constructive Methods for Neural Network Ensembles Based on Two Selective Methods Matching

Twelve methods for constructing two-level selective neural network ensembles are investigated, as are realized by using two selective methods that accomplish two tasks of individual selection respectively. The effects of different corporations of selection methods and individuals' size on two-level selective neural network ensembles are discussed. Experimental results showed that the matching modes of methods of selection and proper individuals' number adopted can ensure the accuracy and diversity of individuals and alleviate the effect of the over-fitting problem. As a consequence, the accuracy of neural network ensembles is improved.