Knowledge Base to Fuzzy Information Granule: A Review from the Interpretability-Accuracy Perspective

Fuzzy information granules indicate sufficiently interpretable fuzzy sets for achieving a high level of human cognitive abstraction. Furthermore, granularity, complexity, and accuracy are associated with fuzzy information granules. Measuring granularity is a promising means of verifying the effectiv...

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Main Authors:	Ahmed, M. M., Nor Ashidi, Mat Isa
Format:	Article
Language:	English
Published:	Elsevier 2017
Subjects:	QA76 Computer software
Online Access:	http://umpir.ump.edu.my/id/eprint/17042/ http://umpir.ump.edu.my/id/eprint/17042/ http://umpir.ump.edu.my/id/eprint/17042/ http://umpir.ump.edu.my/id/eprint/17042/1/Knowledge%20base%20to%20fuzzy%20information%20granule-%20A%20review%20from%20the%20interpretability-accuracy%20perspective.pdf

id	ump-17042
recordtype	eprints
spelling	ump-170422018-02-14T02:24:29Z http://umpir.ump.edu.my/id/eprint/17042/ Knowledge Base to Fuzzy Information Granule: A Review from the Interpretability-Accuracy Perspective Ahmed, M. M. Nor Ashidi, Mat Isa QA76 Computer software Fuzzy information granules indicate sufficiently interpretable fuzzy sets for achieving a high level of human cognitive abstraction. Furthermore, granularity, complexity, and accuracy are associated with fuzzy information granules. Measuring granularity is a promising means of verifying the effectiveness of the fuzzy granular model. Higher granularity indicates fine partitions, whereas coarser partitions suggest lower granularity. Therefore, accuracy is directly proportional to the granularity, such that, the higher the granularity, the more accurate and more complex the model is. Consequently, the granularity-simplicity tradeoff is also a significant criterion in considering the interpretability-accuracy tradeoff. This paper thoroughly reviews diverse ideas to understand the fuzzy information granule and addresses a sensible compromise between interpretability-accuracy and granularity-simplicity. Those requirements contradict each other, thus certain conceptual and mathematical considerations are necessary in designing a granular framework. Moreover, a double axis taxonomy is introduced in this paper: “complexity-based granularity versus semantic-based granularity” (which considers granularity measures) and “granular partition level versus granular rule base level” (regarding knowledge base stages). However, several constraints should be considered in designing a granular framework such as the granularity-accuracy dilemma, the overfitting/underfitting situation, the granular rule base level conflict, the interpretability constraint threshold, the stability-plasticity dilemma, and the parameter optimization. This paper primarily aims to present a conceptual framework to better understand existing methods, as well as how these methods can inspire future research. Elsevier 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/17042/1/Knowledge%20base%20to%20fuzzy%20information%20granule-%20A%20review%20from%20the%20interpretability-accuracy%20perspective.pdf Ahmed, M. M. and Nor Ashidi, Mat Isa (2017) Knowledge Base to Fuzzy Information Granule: A Review from the Interpretability-Accuracy Perspective. Applied Soft Computing, 54. pp. 121-140. ISSN 1568-4946 (print); 1872-9681 (online) https://doi.org/10.1016/j.asoc.2016.12.055 doi: 10.1016/j.asoc.2016.12.055
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Ahmed, M. M. Nor Ashidi, Mat Isa Knowledge Base to Fuzzy Information Granule: A Review from the Interpretability-Accuracy Perspective
description	Fuzzy information granules indicate sufficiently interpretable fuzzy sets for achieving a high level of human cognitive abstraction. Furthermore, granularity, complexity, and accuracy are associated with fuzzy information granules. Measuring granularity is a promising means of verifying the effectiveness of the fuzzy granular model. Higher granularity indicates fine partitions, whereas coarser partitions suggest lower granularity. Therefore, accuracy is directly proportional to the granularity, such that, the higher the granularity, the more accurate and more complex the model is. Consequently, the granularity-simplicity tradeoff is also a significant criterion in considering the interpretability-accuracy tradeoff. This paper thoroughly reviews diverse ideas to understand the fuzzy information granule and addresses a sensible compromise between interpretability-accuracy and granularity-simplicity. Those requirements contradict each other, thus certain conceptual and mathematical considerations are necessary in designing a granular framework. Moreover, a double axis taxonomy is introduced in this paper: “complexity-based granularity versus semantic-based granularity” (which considers granularity measures) and “granular partition level versus granular rule base level” (regarding knowledge base stages). However, several constraints should be considered in designing a granular framework such as the granularity-accuracy dilemma, the overfitting/underfitting situation, the granular rule base level conflict, the interpretability constraint threshold, the stability-plasticity dilemma, and the parameter optimization. This paper primarily aims to present a conceptual framework to better understand existing methods, as well as how these methods can inspire future research.
format	Article
author	Ahmed, M. M. Nor Ashidi, Mat Isa
author_facet	Ahmed, M. M. Nor Ashidi, Mat Isa
author_sort	Ahmed, M. M.
title	Knowledge Base to Fuzzy Information Granule: A Review from the Interpretability-Accuracy Perspective
title_short	Knowledge Base to Fuzzy Information Granule: A Review from the Interpretability-Accuracy Perspective
title_full	Knowledge Base to Fuzzy Information Granule: A Review from the Interpretability-Accuracy Perspective
title_fullStr	Knowledge Base to Fuzzy Information Granule: A Review from the Interpretability-Accuracy Perspective
title_full_unstemmed	Knowledge Base to Fuzzy Information Granule: A Review from the Interpretability-Accuracy Perspective
title_sort	knowledge base to fuzzy information granule: a review from the interpretability-accuracy perspective
publisher	Elsevier
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/17042/ http://umpir.ump.edu.my/id/eprint/17042/ http://umpir.ump.edu.my/id/eprint/17042/ http://umpir.ump.edu.my/id/eprint/17042/1/Knowledge%20base%20to%20fuzzy%20information%20granule-%20A%20review%20from%20the%20interpretability-accuracy%20perspective.pdf
first_indexed	2023-09-18T22:23:15Z
last_indexed	2023-09-18T22:23:15Z
_version_	1777415797104181248

Knowledge Base to Fuzzy Information Granule: A Review from the Interpretability-Accuracy Perspective

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