Parallelized extreme learning machine ensemble based on min–max modular network

Extreme Learning Machine (ELM) as an emergent technology has shown its promising performance in many applications. This paper proposes a parallelized ELM ensemble based on the Min–Max Modular network (M3-network) to meet the challenge of the so-called big data. The proposed M3-ELM first decomposes classification problems into smaller subproblems, then trains an ELM for each subproblem, and in the end ensembles these ELMs with the M3-network. Twelve data sets including both benchmarks and real-world applications are employed to test the proposed method. The experimental results show that M3-ELM not only speeds up the training phrases by 1.6–4.6 times but also reduces the test errors by 0.37–19.51% compared with the normal ELM. The results also indicate that M3-ELM possesses scalability on large-scale tasks and accuracy improvement on imbalanced tasks.