Chaotic emperor penguin optimised extreme learning machine for microarray cancer classification

Microarray technology plays a significant role in cancer classification, where a large number of genes and samples are simultaneously analysed. For the efficient analysis of the microarray data, there is a great demand for the development of intelligent techniques. In this article, the authors propose a novel hybrid technique employing Fisher criterion, ReliefF, and extreme learning machine (ELM) based on the principle of chaotic emperor penguin optimisation algorithm (CEPO). EPO is a recently developed metaheuristic method. In the proposed method, initially, Fisher score and ReliefF are independently used as filters for relevant gene selection. Further, a novel population‐based metaheuristic, namely, CEPO was proposed to pre‐train the ELM by selecting the optimal input weights and hidden biases. The authors have successfully conducted experiments on seven well‐known data sets. To evaluate the effectiveness, the proposed method is compared with original EPO, genetic algorithm, and particle swarm optimisation‐based ELM along with other state‐of‐the‐art techniques. The experimental results show that the proposed framework achieves better accuracy as compared to the state‐of‐the‐art schemes. The efficacy of the proposed method is demonstrated in terms of accuracy, sensitivity, specificity, and F ‐measure.