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