Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie

The performance of a single-model (classifier) can be determined on the basis of the classification accuracy. However, it is difficult to determine which single-model is the best classification technique in a specific application domain since a single learning algorithm may not uniformly outperform...

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Main Author:	Mat Saffie, Nur Amira
Format:	Thesis
Language:	English
Published:	2019
Online Access:	https://ir.uitm.edu.my/id/eprint/87830/1/87830.pdf
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id	my-uitm-ir.87830
record_format	uketd_dc
spelling	my-uitm-ir.878302024-04-11T23:39:22Z Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie 2019 Mat Saffie, Nur Amira The performance of a single-model (classifier) can be determined on the basis of the classification accuracy. However, it is difficult to determine which single-model is the best classification technique in a specific application domain since a single learning algorithm may not uniformly outperform other algorithms over various datasets. Furthermore, most classification algorithms, using either fuzzy or non-fuzzy approaches, produce results in the form of crisp or categorical classification outcomes. Moreover, in certain applications, the classification outcomes that represent class labels may involve categorisations which are fuzzy in nature. 2019 Thesis https://ir.uitm.edu.my/id/eprint/87830/ https://ir.uitm.edu.my/id/eprint/87830/1/87830.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Rasmani, Khairul Anwar
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Rasmani, Khairul Anwar
description	The performance of a single-model (classifier) can be determined on the basis of the classification accuracy. However, it is difficult to determine which single-model is the best classification technique in a specific application domain since a single learning algorithm may not uniformly outperform other algorithms over various datasets. Furthermore, most classification algorithms, using either fuzzy or non-fuzzy approaches, produce results in the form of crisp or categorical classification outcomes. Moreover, in certain applications, the classification outcomes that represent class labels may involve categorisations which are fuzzy in nature.
format	Thesis
qualification_level	Master's degree
author	Mat Saffie, Nur Amira
spellingShingle	Mat Saffie, Nur Amira Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie
author_facet	Mat Saffie, Nur Amira
author_sort	Mat Saffie, Nur Amira
title	Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie
title_short	Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie
title_full	Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie
title_fullStr	Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie
title_full_unstemmed	Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie
title_sort	fuzzy classification based on combinative algorithms with fuzzy similarity measure / nur amira mat saffie
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2019
url	https://ir.uitm.edu.my/id/eprint/87830/1/87830.pdf
_version_	1804889792303857664

Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie

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