20.500.12556/DKUM-96495 An efficient iterative algorithm to explainable feature learning This paper summarizes a doctoral thesis introducing the new iterative approach to explainable feature learning. Features are learned in three steps during each iteration: feature construction, evaluation, and selection. We demonstrated superior performances compared to the state of the art on 13 of 15 test cases and the explainability of the learned feature representation for knowledge discovery. data classification explainable artificial intelligence feature learning klasifikacija podatkov razložljiva umetna inteligenca učenje funkcij true false true Slovenian Society Informatika Angleški jezik Slovenski jezik https://www.informatica.si/index.php/informatica Članek v reviji 2026-01-14 14:03:32 2026-01-14 14:03:33 2026-01-15 03:10:12 0000-00-00 00:00:00 2024 0 0 str. 289-290 iss. 2 Vol. 48 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-04-23 0000-00-00 2024-05-28 004.6 204109315 0350-5596 6105-Article_Text-14555-17181-10-20240724.pdf 6105-Article_Text-14555-17181-10-20240724.pdf 1 AFFC46415B730A42DD2A9626874FB3B4 794b4228112540924425448bc23f3a72aa1bed96a50c3415b37149b3ceb45e99 c4696a58-f149-11f0-8a93-00155d580e02 https://dk.um.si/Dokument.php?lang=slv&id=202009 https://www.informatica.si/index.php/informatica/article/view/6105 1 6fb51c4d-f149-11f0-8a93-00155d580e02 https://dk.um.si/Dokument.php?lang=slv&id=202008 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0