Interpretable and Reliable Rule Classification Based on Conformal Prediction

Autor: Abdelqader, H., Smirnov, E., Pont, M., Geijselaers, M., Koprinska, I, Mignone, P, Guidotti, R, Jaroszewicz, S, Froning, H, Gullo, F, Ferreira, PM, Roqueiro, D, Ceddia, G, Nowaczyk, S, Gama, J, Ribeiro, R, Gavalda, R, Masciari, E, Ras, Z, Ritacco, E, Naretto, F, Theissler, A, Biecek, P, Verbeke, W, Schiele, G, Pernkopf, F, Blott, M, Bordino, I, Danesi, IL, Ponti, G, Severini, L, Appice, A, Andresini, G, Medeiros, I, Graca, G, Cooper, L, Ghazaleh, N, Richiardi, J, Saldana, D, Sechidis, K, Canakoglu, A, Pido, S, Pinoli, P, Bifet, A, Pashami, S

Publikováno v: MACHINE LEARNING AND PRINCIPLES AND PRACTICE OF KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2022, PT I, 1752, 385-401
Communications in Computer and Information Science ISBN: 9783031236174

This paper deals with the challenging problem of simultaneously integrating interpretablility and reliability into prediction models in machine learning. It proposes to combine the interpretable models of decision rules with the reliable models based

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff8f9296d85ce134932aec8ce39f83cb
https://doi.org/10.1007/978-3-031-23618-1_26

BoostEMM : Transparent boosting using exceptional model mining

Autor: van der Zon, S.B., Zeev Ben Mordehay, O., Vrijdag, T.S., van Ipenburg, W., Veldsink, J., Duivesteijn, W., Pechenizkiy, M., Bordino, I., Caldarelli, G., Fumarola, F., Gullo, F., Squartini, T.

Publikováno v: Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017), 18 September 2017, Skopje, Macedonia, 5-16
STARTPAGE=5;ENDPAGE=16;TITLE=Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017), 18 September 2017, Skopje, Macedonia

Boosting is an iterative ensemble-learning paradigm. Every iteration, a weak predictor learns a classification task, taking into account performance achieved in previous iterations. This is done by assigning weights to individual records of the datas

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::be52bf3a4cb42b18d9a9de9abfb1c462
https://research.tue.nl/nl/publications/ad232d69-ed50-4be9-98c2-fac7a6561560

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Temporal Evolution of the UK Web.

Autor: Bordino, I., Boldi, P., Donato, D., Santini, M., Vigna, S.

Publikováno v: 2008 IEEE International Conference on Data Mining Workshops; 2008, p909-918, 10p

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