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http://hdl.handle.net/1942/33621
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DC Field | Value | Language |
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dc.contributor.author | BELLO GARCIA, Marilyn | - |
dc.contributor.author | NAPOLES RUIZ, Gonzalo | - |
dc.contributor.author | VANHOOF, Koen | - |
dc.contributor.author | Bello, Rafael | - |
dc.date.accessioned | 2021-03-03T09:08:37Z | - |
dc.date.available | 2021-03-03T09:08:37Z | - |
dc.date.issued | 2021 | - |
dc.date.submitted | 2021-03-02T21:54:24Z | - |
dc.identifier.citation | Information Sciences, 560 , p. 51 -67 | - |
dc.identifier.issn | 0020-0255 | - |
dc.identifier.uri | http://hdl.handle.net/1942/33621 | - |
dc.description.abstract | Rough set theory is a granular computing formalism that allows analyzing a given dataset through well-defined measures. Some of these measures aim to characterize datasets used to discover knowledge, mostly in traditional classification problems. Measuring the data quality is pivotal to estimate beforehand the problem's difficulty since a classification mod-el's accuracy heavily depends on the data quality. However, to the best of our knowledge, there are no measures devoted to analyzing the quality of multi-label datasets. In this paper, we propose six data quality measures for multi-label problems, which are based on different granular approaches. Some of these measures redefine the decision class concept , while others redefine the consistency concept. Moreover, we study the impact of the similarity threshold parameters and the distance functions on the behavior of these measures. The numerical simulations show a statistical correlation between the measures that redefine the consistency concept and the performance of the ML-kNN classifier. | - |
dc.description.sponsorship | The authors would like to thank the anonymous reviewers for their valuable and constructive feedback. | - |
dc.language.iso | en | - |
dc.publisher | ELSEVIER SCIENCE INC | - |
dc.rights | 2021 Elsevier Inc. All rights reserved. | - |
dc.subject.other | Multi-label classification | - |
dc.subject.other | Granular computing | - |
dc.subject.other | Rough set theory | - |
dc.subject.other | Data quality measures | - |
dc.title | Data quality measures based on granular computing for multi-label classification | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 67 | - |
dc.identifier.spage | 51 | - |
dc.identifier.volume | 560 | - |
local.format.pages | 17 | - |
local.bibliographicCitation.jcat | A1 | - |
local.publisher.place | STE 800, 230 PARK AVE, NEW YORK, NY 10169 USA | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.identifier.doi | 10.1016/j.ins.2021.01.027 | - |
dc.identifier.isi | WOS:000670877900004 | - |
dc.identifier.eissn | 1872-6291 | - |
local.provider.type | CrossRef | - |
local.uhasselt.uhpub | yes | - |
local.uhasselt.international | yes | - |
item.contributor | BELLO GARCIA, Marilyn | - |
item.contributor | NAPOLES RUIZ, Gonzalo | - |
item.contributor | VANHOOF, Koen | - |
item.contributor | Bello, Rafael | - |
item.validation | ecoom 2022 | - |
item.fulltext | With Fulltext | - |
item.accessRights | Open Access | - |
item.fullcitation | BELLO GARCIA, Marilyn; NAPOLES RUIZ, Gonzalo; VANHOOF, Koen & Bello, Rafael (2021) Data quality measures based on granular computing for multi-label classification. In: Information Sciences, 560 , p. 51 -67. | - |
crisitem.journal.issn | 0020-0255 | - |
crisitem.journal.eissn | 1872-6291 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
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1-s2.0-S0020025521000542-main.pdf Restricted Access | Published version | 741.26 kB | Adobe PDF | View/Open Request a copy |
manuscript.pdf | Peer-reviewed author version | 752.99 kB | Adobe PDF | View/Open |
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