Evolutionary Voting-Based Extreme Learning Machines

Nan Liu, Jiuwen Cao, Zhiping Lin, Pin Pin Pek, Zhi Xiong Koh and Marcus Eng Hock Ong

Mathematical Problems in Engineering, 2014, vol. 2014, 1-7

Abstract:

Voting-based extreme learning machine (V-ELM) was proposed to improve learning efficiency where majority voting was employed. V-ELM assumes that all individual classifiers contribute equally to the decision ensemble. However, in many real-world scenarios, this assumption does not work well. In this paper, we aim to enhance V-ELM by introducing weights to distinguish the importance of each individual ELM classifier in decision making. Genetic algorithm is used for optimizing these weights. This evolutionary V-ELM is named as EV-ELM. Results on several benchmark databases show that EV-ELM achieves the highest classification accuracy compared with V-ELM and ELM.

Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:808292

DOI: 10.1155/2014/808292

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