Optimal Weighted Extreme Learning Machine for Imbalanced Learning with Differential Evolution

In this paper, we present a formal model for the optimal weighted extreme learning machine (ELM) on imbalanced learning. Our model regards the optimal weighted ELM as an optimization problem to find the best weight matrix. We propose an approximate search algorithm, named weighted ELM with differential evolution (DE), that is a competitive stochastic search technique, to solve the optimization problem of the proposed formal imbalanced learning model. We perform experiments on standard imbalanced classification datasets which consist of 39 binary datasets and 3 multiclass datasets. The results show a significant performance improvement over standard ELM with an average Gmean improvement of 10.15% on binary datasets and 1.48% on multiclass datasets, which are also better than other state-of-the-art methods. We also demonstrate that our proposed algorithm can achieve high accuracy in representation learning by performing experiments on MNIST, CIFAR-10, and YouTube-8M, with feature representation from convolutional neural networks.