Surrogate-Assisted Multi-objective Genetic Fuzzy Associative Classification by Multiple Granularity Measures-Reference-Cited by-同舟云学术

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

Published:2023-01-24 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference for Advancement in Technology (ICONAT)
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Author:

Behera Ajit Kumar¹,Dehuri Satchidananda²,Ghosh Ashish³

Affiliation:

1. Silicon Institute of Technology,Department of Computer Science & Engineering,Bhubaneswar,India

2. Fakir Mohan University,Department of Computer Science,Balasore,India

3. Indian Statistical Institute,Machine Intelligence Unit,203 B.T. Road, Kolkata,India,700108

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10079844/10079947/10080059.pdf?arnumber=10080059

Reference34 articles.

1. Revisiting Evolutionary Fuzzy Systems: Taxonomy, applications, new trends and challenges

2. A novel associative classification model based on a fuzzy frequent pattern mining algorithm

3. Improving a Fuzzy Association Rule-Based Classification Model by Granularity Learning Based on Heuristic Measures over Multiple Granularities;fazzolani;In Proceedings of 20B IEEE International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS),2011

4. Surrogate-Assisted Multi-objective Genetic Algorithms for Fuzzy Rule-Based Classification

5. Multi-objective Classification Rule Mining Using Gene Expression Programming

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