Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Koutsoviti Koumeri, Lisa"'
Autor:
Nápoles, Gonzalo, Grau, Isel, Concepción, Leonardo, Koutsoviti Koumeri, Lisa, Papa, João Paulo
Publikováno v:
In Neurocomputing 7 April 2022 481:33-45
Publikováno v:
arXiv, 2023:2305.09399v2, 1-20. Cornell University Library
In this paper, we integrate the concepts of feature importance with implicit bias in the context of pattern classification. This is done by means of a three-step methodology that involves (i) building a classifier and tuning its hyperparameters, (ii)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f5b5c217a6b9e8f22c1ba4d5e176870
Publikováno v:
In Pattern Recognition Letters February 2022 154:29-36
Autor:
Koutsoviti Koumeri, Lisa, Nápoles, Gonzalo, Tavares, João Manuel R. S., Papa, João Paulo, González Hidalgo, Manuel
Publikováno v:
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783030934194
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 351-360
STARTPAGE=351;ENDPAGE=360;TITLE=Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 351-360
STARTPAGE=351;ENDPAGE=360;TITLE=Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
The need to measure and mitigate bias in machine learning data sets has gained wide recognition in the field of Artificial Intelligence (AI) during the past decade. The academic and business communities call for new general-purpose measures to quanti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f75df9bbe8c5b32eef0ac63acd9e9267
https://doi.org/10.1007/978-3-030-93420-0_33
https://doi.org/10.1007/978-3-030-93420-0_33
Publikováno v:
arXiv, 2021:2112.12713. Cornell University Library
Neurocomputing, 481, 33-45. Elsevier Science BV
Pure TUe
Neurocomputing, 481, 33-45. Elsevier
Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
Neurocomputing, 481, 33-45. Elsevier Science BV
Pure TUe
Neurocomputing, 481, 33-45. Elsevier
Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
Made available in DSpace on 2022-05-01T13:11:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-04-07 This paper presents a Fuzzy Cognitive Map model to quantify implicit bias in structured datasets where features can be numeric or discrete. I
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8c1b448147f6ca86def7026d47ab6c04
https://research.tue.nl/nl/publications/4843cde8-64e0-4320-afc1-3071cc870481
https://research.tue.nl/nl/publications/4843cde8-64e0-4320-afc1-3071cc870481
Publikováno v:
Pattern Recognition Letters, 154, 29-36. ELSEVIER SCI LTD
The need to measure bias encoded in tabular data that are used to solve pattern recognition problems is widely recognized by academia, legislators and enterprises alike. In previous work, we proposed a bias quantification measure, called fuzzy-rough
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ead4b019afb5c40ab166affcce3cdce7
http://arxiv.org/abs/2108.09098
http://arxiv.org/abs/2108.09098