Evolutionary Voting‐Based Extreme Learning Machines
Nan Liu, Jiuwen Cao, Zhiping Lin, Pin Pin Pek, Zhi Xiong Koh, Marcus Eng Hock Ong · Mathematical Problems in Engineering · 2014
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.