Surrogate-Assisted Multi-objective Genetic Fuzzy Associative Classification by Multiple Granularity Measures

Authors

Ajit Kumar Behera, Silicon Institute of Technology, Bhubaneswar
Satchidananda Dehuri, Fakir Mohan University
Ashish Ghosh, Indian Statistical Institute, Kolkata

Document Type

Conference Article

Publication Title

2023 International Conference for Advancement in Technology, ICONAT 2023

Abstract

This paper presents a new surrogate-assisted multi-objective genetic fuzzy associative classification model by learning multiple granularities. The specific method is the hybridization of multi-objective genetic algorithms (MOGAs), radial basis function neural networks (RBFNs), and rough set. We show that our approach requires only a few numbers of fitness evaluations compared to the methods proposed in [34] without compromising to maintain an average classification ability in almost all the datasets considered in this work for evaluation of the model.

DOI

10.1109/ICONAT57137.2023.10080059

Publication Date

1-1-2023

Recommended Citation

Behera, Ajit Kumar; Dehuri, Satchidananda; and Ghosh, Ashish, "Surrogate-Assisted Multi-objective Genetic Fuzzy Associative Classification by Multiple Granularity Measures" (2023). Conference Articles. 612.
https://digitalcommons.isical.ac.in/conf-articles/612