Optimal feature subset selection for fuzzy extreme learning machine using genetic algorithm with multilevel parameter optimization

Abstract

The crucial objective of this paper is to design a hybrid model of the genetic algorithm for fuzzy extreme learning machine classifier (GA-FELM), which selects an optimal feature subset by using the multilevel parameter optimization technique. Feature subset selection is an important task in pattern classification and knowledge discovery problems. The generalization performance of the system is not only depending on optimal features but also dependent upon the classifier (learning algorithm). Therefore, it is an important task to select a fast and efficient classifier. Research efforts have affirmed that extreme learning machine (ELM) has superior and accurate classification ability. However, ELM is failed to handle the uncertain data. One of the alternative solutions is fuzzy-ELM, which combines the advantages of fuzzy logic and ELM. GA-FELM is able to handle curse of dimensionality problem, optimization problem and weighted classification problem with maximizing classification accuracy by minimizing the number of features. In order to validate the efficiency of GA-FELM, the comparative performance is evaluated by using three different approaches viz. 1. ELM and GA-ELM 2. GA-ELM and GA-FELM 3. GA-FELM and GA-existing classifier. The result analysis shows that classification accuracy is improved with 9% while reducing 62% features.