An Empirical Study of Fuzzy Decision Tree for Gradient Boosting Ensemble

Liu, Z; Liu, A; Zhang, G; Lu, J

An Empirical Study of Fuzzy Decision Tree for Gradient Boosting Ensemble

Liu, Z Liu, A

Zhang, G

Lu, J

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Publisher:: SPRINGER INTERNATIONAL PUBLISHING AG
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13151 LNAI, pp. 716-727
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Liu, Z
dc.contributor.author	Liu, A https://orcid.org/0000-0002-0733-7138
dc.contributor.author	Zhang, G https://orcid.org/0000-0003-3960-0583
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732
dc.contributor.editor	Long, G
dc.contributor.editor	Yu, X
dc.contributor.editor	Wang, S
dc.date	2022-02-02
dc.date.accessioned	2023-03-21T00:59:30Z
dc.date.available	2023-03-21T00:59:30Z
dc.date.issued	2022-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13151 LNAI, pp. 716-727
dc.identifier.isbn	9783030975456
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/167866
dc.description.abstract	Gradient boosting has been proved to be an effective ensemble learning paradigm to combine multiple weak learners into a strong one. However, its improved performance is still limited by decision errors caused by uncertainty. Fuzzy decision trees are designed to solve the uncertainty problems caused by the collected information’s limitation and incompleteness. This paper investigates whether the robustness of gradient boosting can be improved by using fuzzy decision trees even when the decision conditions and objectives are fuzzy. We first propose and implement a fuzzy decision tree (FDT) by referring to two widely cited fuzzy decision trees. Then we propose and implement a fuzzy gradient boosting decision tree (FGBDT), which integrates a set of FDTs as weak learners. Both the algorithms can be set as non-fuzzy algorithms by parameters. To study whether fuzzification can improve the proposed algorithms in classification tasks, we pair the algorithms with their non-fuzzy algorithms and run comparison experiments on UCI Repository datasets in the same settings. The experiments show that the fuzzy algorithms perform better than their non-fuzzy algorithms in many classical classification tasks. The code is available at github.com/ZhaoqingLiu/FuzzyTrees.
dc.language	en
dc.publisher	SPRINGER INTERNATIONAL PUBLISHING AG
dc.relation	http://purl.org/au-research/grants/arc/DP190101733
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.ispartof	34th Australasian Joint Conference on Artificial Intelligence (AI)
dc.relation.ispartofseries	Lecture Notes in Artificial Intelligence
dc.relation.isbasedon	10.1007/978-3-030-97546-3_58
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	This is a post-peer-review, pre-copyedit version of an article published in [Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2022, 13151 LNAI, pp. 716-727] The final authenticated version is available online at: [ http://doi.org/10.1007/978-3-030-97546-3_58]”
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	An Empirical Study of Fuzzy Decision Tree for Gradient Boosting Ensemble
dc.type	Conference Proceeding
utslib.citation.volume	13151 LNAI
utslib.location.activity	Univ Technol Sydney, ELECTR NETWORK
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-21T00:59:29Z
pubs.finish-date	2022-02-04
pubs.publication-status	Published
pubs.start-date	2022-02-02
pubs.volume	13151 LNAI

Abstract:

Gradient boosting has been proved to be an effective ensemble learning paradigm to combine multiple weak learners into a strong one. However, its improved performance is still limited by decision errors caused by uncertainty. Fuzzy decision trees are designed to solve the uncertainty problems caused by the collected information’s limitation and incompleteness. This paper investigates whether the robustness of gradient boosting can be improved by using fuzzy decision trees even when the decision conditions and objectives are fuzzy. We first propose and implement a fuzzy decision tree (FDT) by referring to two widely cited fuzzy decision trees. Then we propose and implement a fuzzy gradient boosting decision tree (FGBDT), which integrates a set of FDTs as weak learners. Both the algorithms can be set as non-fuzzy algorithms by parameters. To study whether fuzzification can improve the proposed algorithms in classification tasks, we pair the algorithms with their non-fuzzy algorithms and run comparison experiments on UCI Repository datasets in the same settings. The experiments show that the fuzzy algorithms perform better than their non-fuzzy algorithms in many classical classification tasks. The code is available at github.com/ZhaoqingLiu/FuzzyTrees.

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http://hdl.handle.net/10453/167866