Fuzzy Systems and its Applications

Fuzzy Systems and its Applications

Improving classification accuracy of multi-label data using classical fuzzy relation in ant colony optimizer

Document Type : Original Article

Authors
Vahid Mehrdad 1
sahar shams beyranvand 2
Mohammad Bagher Dowlatshahi 3
1 Department of Electrical and Electronics Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran
2 Faculty of Electrical Engineering, Lorestan University, Khorramabad, Iran
3 Department of Computer Engineering, Faculty of Engineering, Lorestan University, Khorramabad, Iran.
10.22034/jfsa.2024.465088.1221
Abstract
This paper proposes a novel approach to enhance the accuracy of multi-label data classification by integrating classical fuzzy logic with the Ant Colony Optimization (ACO) algorithm based on reinforcement learning. The increasing application of multi-label data in various domains necessitates effective methods to improve classification accuracy. In this method, classical fuzzy logic is combined with the ACO algorithm based on reinforcement learning to enhance its ability to handle the complex relationships inherent in multi-label datasets. Additionally, the integration of feature selection techniques with the ACO algorithm based on reinforcement learning is utilized to identify and exploit the most relevant features, resulting in dimensionality reduction and improved computational efficiency. Extensive experiments on standard datasets demonstrate the effectiveness of this approach in achieving better accuracy compared to competing methods. The proposed framework not only improves classification performance but also provides insights into the interpretability of classification decisions, contributing to the advancement of multi-label data classification methods.
Keywords
Machine learning
ant colony optimizer
feature selection
fuzzy algorithm
multi-label data
Subjects
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Fuzzy Systems and its Applications
Volume 7, Issue 2 - Serial Number 15
Open Access Statement
December 2024
Pages 87-109

  • Receive Date 27 June 2024
  • Revise Date 14 September 2024
  • Accept Date 06 October 2024