A Comprehensive Study of the Efficiency of Type-Reduction Algorithms

Chen, Chao; Wu, Dongrui; Garibaldi, Jonathan M.; John, Robert I.; Twycross, Jamie; Mendel, Jerry M.

Abstract

Improving the efficiency of type-reduction algorithms continues to attract research interest. Recently, there have been some new type-reduction approaches claiming that they are more efficient than the well-known algorithms such as the enhanced Karnik-Mendel (EKM) and the enhanced iterative algorithm with stopping condition (EIASC). In a previous paper, we found that the computational efficiency of an algorithm is closely related to the platform, and how it is implemented. In computer science, the dependence on languages is usually avoided by focusing on the complexity of algorithms (using big O notation). In this paper, the main contribution is the proposal of two novel type-reduction algorithms. Also, for the first time, a comprehensive study on both existing and new type-reduction approaches is made based on both algorithm complexity and practical computational time under a variety of programming languages. Based on the results, suggestions are given for the preferred algorithms in different scenarios depending on implementation platform and application context.

Citation

Chen, C., Wu, D., Garibaldi, J. M., John, R. I., Twycross, J., & Mendel, J. M. (2021). A Comprehensive Study of the Efficiency of Type-Reduction Algorithms. IEEE Transactions on Fuzzy Systems, 29(6), 1556 -1566. https://doi.org/10.1109/tfuzz.2020.2981002

• Journal Article Type: Article
• Acceptance Date: Mar 16, 2020
• Online Publication Date: Mar 16, 2020
• Publication Date: 2021-06
• Deposit Date: Apr 7, 2020
• Publicly Available Date: Apr 7, 2020
• Journal: IEEE Transactions on Fuzzy Systems
• Print ISSN: 1063-6706
• Electronic ISSN: 1941-0034
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 29
• Issue: 6
• Pages: 1556 -1566
• DOI: https://doi.org/10.1109/tfuzz.2020.2981002
• Public URL: https://nottingham-repository.worktribe.com/output/4264940
• Publisher URL: https://ieeexplore.ieee.org/document/9037123
• Additional Information: © 2020 IEEE.Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.