Bin Muda, M.Z. and Panoutsos, G. (2021) An entropy-based uncertainty measure for developing granular models. In: Proceedings of 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI). 2020 7th International Conference on Soft Computing & Machine Intelligence (ISCMI), 14-15 Nov 2020, Stockholm, Sweden (Online conference). IEEE , pp. 73-77. ISBN 9781728175607
Abstract
There are two main ways to construct Fuzzy Logic rule-based models: using expert knowledge and using data mining methods. One of the most important aspects of Granular Computing (GrC) is to discover and extract knowledge from raw data in the form of information granules. The knowledge gained from the GrC, the information granules, can be used in constructing the linguistic rule-bases of a Fuzzy-Logic based system. Algorithms for iterative data granulation in the literature, so far, do not account for data uncertainty during the granulation process. In this paper, the uncertainty during the data granulation process is captured using the fundamental concept in information theory, entropy. In the proposed GrC algorithm, data granules are defined as information objects, hence the entropy measure being used in this research work is to capture the uncertainty in the data vectors resulting from the merging of the information granules. The entropy-based uncertainty measure is used to guide the iterative granulation process, hence promoting the formation of new granules with less uncertainty. The enhanced information granules are then being translated into a Fuzzy Logic inference system. The effectiveness of the proposed approach is demonstrated using established datasets.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Granular models; Fuzzy Logic; Information Theory; Granular Computing |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Aug 2020 08:10 |
Last Modified: | 07 Jan 2022 01:39 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ISCMI51676.2020.9311589 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:164270 |