Cost-sensitive ELM based on Reduced Expectation Kernel Auto-encoder

ELM has drawn great attention due its high training speed and outstanding generalization performance. To solve the problem that the long training time of kernel ELM auto-encoder and the difficult setting of the weight of kernel function in the existing multi-kernel models, a cost-sensitive ELM method based on expectation kernel is proposed. Firstly, from the view of similarity, the reduced kernel auto-encoder is defined by randomly selecting the reference points from the input data; then, the reduced expectation kernel auto-encoder is designed according to the expectation kernel ELM, and the combination of random mapping and similarity mapping is realized. On this basis, the parallel multi-kernel ELM model is designed, and the output of the classifier is converted into posterior probability. Finally, the cost-sensitive decision is realized based on the minimum risk criterion. The experimental results on the reference dataset verify the effectiveness of the method.