Performance enhancement of extreme learning machine for multi-category sparse data classification problems
This paper presents a performance enhancement scheme for the recently developed extreme learning machine (ELM) for multi-category sparse data classification problems. ELM is a single hidden layer neural network with good generalization capabilities and extremely fast learning capacity. In ELM, the input weights are randomly chosen and the output weights are analytically calculated. The generalization performance of the ELM algorithm for sparse data classification problem depends critically on three free parameters. They are, the number of hidden neurons, the input weights and the bias values which need to be optimally chosen. Selection of these parameters for the best performance of ELM involves a complex optimization problem. In this paper, we present a new, real-coded genetic algorithm approach called RCGA-ELM to select the optimal number of hidden neurons, input weights and bias values which results in better performance. Two new genetic operators called network based operator and weight based operator are proposed to find a compact network with higher generalization performance. We also present an alternate and less computationally intensive approach called sparse-ELM. Sparse-ELM searches for the best parameters of ELM using K-fold validation. A multi-class human cancer classification problem using micro-array gene expression data (which is sparse), is used for evaluating the performance of the two schemes. Results indicate that the proposed RCGA-ELM and sparse-ELM significantly improve ELM performance for sparse multi-category classification problems.
extreme learning machine neural network genetic algorithm k-fold validation micro-array gene expression data multi-category sparse classification