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Introducing positive discrimination in predictive models

Autor: Verwer, S.E., Calders, T., Custers, B., Schermer, B., Zarsky, T.

Publikováno v: Discrimination and Privacy in the Information Society: Effects of Data Mining and Profiling Large Databases, 255-270
STARTPAGE=255;ENDPAGE=270;TITLE=Discrimination and Privacy in the Information Society: Effects of Data Mining and Profiling Large Databases
3, 255-270. Berlin : Springer
STARTPAGE=255;ENDPAGE=270;TITLE=3
Studies in Applied Philosophy, Epistemology and Rational Ethics ISBN: 9783642304866
Discrimination and Privacy in the Information Society
Custers, B.; Calders, T.; Schermer, B. (ed.), Discrimination and Privacy in the Information Society, pp. 255-270

In this chapter we give three solutions for the discrimination-aware classification problem that are based upon Bayesian classifiers. These classifiers model the complete probability distribution by making strong independence assumptions. First we di

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::44eec1c0b104698d86575a110c4f061d
https://doi.org/10.1007/978-3-642-30487-3_14

Quick inclusion-exclusion

Autor: Calders, T., Goethals, B., Bonchi, F., Boulicaut, J.F.

Publikováno v: Lecture Notes in Computer Science ISBN: 9783540332923
KDID
Knowledge discovery in inductive databases : 4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005 : revised selected papers, 86-103
STARTPAGE=86;ENDPAGE=103;TITLE=Knowledge discovery in inductive databases : 4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005 : revised selected papers

Many data mining algorithms make use of the well-known Inclusion-Exclusion principle. As a consequence, using this principle efficiently is crucial for the success of all these algorithms. Especially in the context of condensed representations, such

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::365ea5f83cb46957b71a41b09d9bc4d6
https://doi.org/10.1007/11733492_6

Matching and Maximizing? A neurally plausible model of stochastic reinforcement learning

Autor: Vroon, J.H., Rooij, I.J.E.I. van, Sprinkhuizen-Kuyper, I.G., Calders, T., Tuyls, K., Pechenizkiy, M.

Publikováno v: Calders, T.; Tuyls, K.; Pechenizkiy, M. (ed.), Proceedings of the 21st Benelux Conference on Artificial Intelligence (BNAIC 2009), 257-264. [S.l.] : [S.n.]
STARTPAGE=257;ENDPAGE=264;TITLE=Calders, T.; Tuyls, K.; Pechenizkiy, M. (ed.), Proceedings of the 21st Benelux Conference on Artificial Intelligence (BNAIC 2009)
Calders, T.; Tuyls, K.; Pechenizkiy, M. (ed.), Proceedings of the 21st Benelux Conference on Artificial Intelligence (BNAIC 2009), pp. 257-264

Contains fulltext : 77434.pdf (Publisher’s version ) (Open Access)

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::1065eae7c734a025fc972c63a2c13193
http://hdl.handle.net/2066/77434