Evolutionary Voting-Based Extreme Learning Machines

Abstract EN

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.