基于互信息的选择性集成核极端学习机

针对集成学习中的准确性和差异性平衡问题, 提出一种基于信息论的选择性集成核极端学习机. 采用具有结构简单、训练简便、泛化性能好的核极端学习作为基学习器. 引入相关性准则描述准确性, 冗余性准则描述差异性,将选择性集成问题转化为变量选择问题. 利用基于互信息的最大相关最小冗余准则对生成的核极端学习机进行选择, 从而实现准确性和差异性的平衡. 基于UCI 基准回归和分类数据的仿真结果验证了所提出算法的优越性.

Selective ensemble of extreme learning machine with kernels based on mutual information

Considering the accuracy and diverse balance problem of ensemble learning, a selective ensemble extreme learning machine with kernels based on mutual information is proposed. The extreme learning machine with kernels, which has characteristics of simple structure, fast training and good generalization, is chosen as the base learner. The correlation and redundancy criterion are introduced to describe the accuracy and diverse, and the selective ensemble problem is transformed as a variable problem. Then, the maximum correlation minimum redundancy criterion based on mutual information is used to select the extreme learning machine with kernels. The simulation results based on UCI benchmark regression and classification data show the advantages of the proposed algorithm.