Genetic Algorithm based Data Analysis for Fuzzy Extreme Learning Machine

Abstract

The crucial objective of this paper is to focus on the research work which aims 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.  Data analysis 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 failed to handle the uncertain data and weighted classification problem. 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 performance of GA-FELM, the comparison is made with three approaches viz. 1. ELM and GA-ELM 2. GA-ELM and GA-FELM 3. GA-FELM and GA-Existing classifier. The comparative analysis shows that classification accuracy is improved with 9% by reducing 62% features.