Explaining dimensionality reduction results using Shapley values

Dimensionality reduction (DR) techniques have been consistently supporting high-dimensional data analysis in various applications. Besides the patterns uncovered by these techniques, the interpretation of DR results based on each feature's contribution to the low-dimensional representation supp...

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Bibliographic Details
Authors: Marcílio-Jr, Wilson E. [UNESP], Eler, Danilo M. [UNESP]
Format: article
Status:Published version
Publication Date:2021
Country:Brasil
Institution:Universidade Estadual Paulista (UNESP)
Repository:Repositório Institucional da UNESP
Language:English
OAI Identifier:oai:repositorio.unesp.br:11449/206269
Online Access:http://dx.doi.org/10.1016/j.eswa.2021.115020
http://hdl.handle.net/11449/206269
Access Level:Open access
Keyword:Dimensionality reduction
Explainability
Shapley values
Visualization
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spelling Explaining dimensionality reduction results using Shapley valuesDimensionality reductionExplainabilityShapley valuesVisualizationDimensionality reduction (DR) techniques have been consistently supporting high-dimensional data analysis in various applications. Besides the patterns uncovered by these techniques, the interpretation of DR results based on each feature's contribution to the low-dimensional representation supports new finds through exploratory analysis. Current literature approaches designed to interpret DR techniques do not explain the features’ contributions well since they focus only on the low-dimensional representation or do not consider the relationship among features. This paper presents ClusterShapley to address these problems, using Shapley values to generate explanations of dimensionality reduction techniques and interpret these algorithms using a cluster-oriented analysis. ClusterShapley explains the formation of clusters and the meaning of their relationship, which is useful for exploratory data analysis in various domains. We propose novel visualization techniques to guide the interpretation of features’ contributions on clustering formation and validate our methodology through case studies of publicly available datasets. The results demonstrate our approach's interpretability and analysis power to generate insights about pathologies and patients in different conditions using DR results.Faculty of Sciences and Technology São Paulo State University (UNESP)Faculty of Sciences and Technology São Paulo State University (UNESP)Universidade Estadual Paulista (Unesp)2021-06-25T10:29:21Z2021-06-25T10:29:21Z2021-09-15info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1016/j.eswa.2021.115020Expert Systems with Applications, v. 178.0957-4174http://hdl.handle.net/11449/20626910.1016/j.eswa.2021.1150202-s2.0-85105034882Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengExpert Systems with Applicationsinfo:eu-repo/semantics/openAccessMarcílio-Jr, Wilson E. [UNESP]Eler, Danilo M. [UNESP]2021-10-23T02:54:11Zoai:repositorio.unesp.br:11449/206269Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestrepositoriounesp@unesp.bropendoar:29462021-10-23T02:54:11Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Explaining dimensionality reduction results using Shapley values
title Explaining dimensionality reduction results using Shapley values
spellingShingle Explaining dimensionality reduction results using Shapley values
Marcílio-Jr, Wilson E. [UNESP]
Dimensionality reduction
Explainability
Shapley values
Visualization
title_short Explaining dimensionality reduction results using Shapley values
title_full Explaining dimensionality reduction results using Shapley values
title_fullStr Explaining dimensionality reduction results using Shapley values
title_full_unstemmed Explaining dimensionality reduction results using Shapley values
title_sort Explaining dimensionality reduction results using Shapley values
dc.creator.none.fl_str_mv Marcílio-Jr, Wilson E. [UNESP]
Eler, Danilo M. [UNESP]
author Marcílio-Jr, Wilson E. [UNESP]
author_facet Marcílio-Jr, Wilson E. [UNESP]
Eler, Danilo M. [UNESP]
author_role author
author2 Eler, Danilo M. [UNESP]
author2_role author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (Unesp)
dc.subject.por.fl_str_mv Dimensionality reduction
Explainability
Shapley values
Visualization
topic Dimensionality reduction
Explainability
Shapley values
Visualization
description Dimensionality reduction (DR) techniques have been consistently supporting high-dimensional data analysis in various applications. Besides the patterns uncovered by these techniques, the interpretation of DR results based on each feature's contribution to the low-dimensional representation supports new finds through exploratory analysis. Current literature approaches designed to interpret DR techniques do not explain the features’ contributions well since they focus only on the low-dimensional representation or do not consider the relationship among features. This paper presents ClusterShapley to address these problems, using Shapley values to generate explanations of dimensionality reduction techniques and interpret these algorithms using a cluster-oriented analysis. ClusterShapley explains the formation of clusters and the meaning of their relationship, which is useful for exploratory data analysis in various domains. We propose novel visualization techniques to guide the interpretation of features’ contributions on clustering formation and validate our methodology through case studies of publicly available datasets. The results demonstrate our approach's interpretability and analysis power to generate insights about pathologies and patients in different conditions using DR results.
publishDate 2021
dc.date.none.fl_str_mv 2021-06-25T10:29:21Z
2021-06-25T10:29:21Z
2021-09-15
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1016/j.eswa.2021.115020
Expert Systems with Applications, v. 178.
0957-4174
http://hdl.handle.net/11449/206269
10.1016/j.eswa.2021.115020
2-s2.0-85105034882
url http://dx.doi.org/10.1016/j.eswa.2021.115020
http://hdl.handle.net/11449/206269
identifier_str_mv Expert Systems with Applications, v. 178.
0957-4174
10.1016/j.eswa.2021.115020
2-s2.0-85105034882
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Expert Systems with Applications
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv repositoriounesp@unesp.br
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score 15,300719

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