20.500.12556/RUL-154950 CLUSPLUS a decision tree-based framework for predicting structured outputs We present CLUSPLUS, a machine learning framework based on decision trees specialized for complex predictive modeling tasks. We provide the scientific community with an open source Java framework that unifies several major research directions in the machine learning field. The framework supports multi-target prediction, i.e., the simultaneous prediction of multiple continuous values, multiple discrete values, and hierarchically organized discrete values. Furthermore, CLUSPLUS enables state-of-the-art predictive performance via ensemble learning, exploitation of unlabeled data via semi-supervised learning, and data understanding via feature importance and building interpretable models. Out of a wide array of machine learning frameworks available today, very few support complex predictive modeling tasks and, to the best of our knowledge, none support all of the aforementioned functionalities. machine learning multi-target regression multi-label classification decision trees feature importance semi-supervised learning random forests strojno učenje klasifikacija označevanje true false true Angleški jezik Slovenski jezik Članek v reviji 2024-03-11 14:36:14 2024-03-11 14:36:18 2024-03-12 03:52:19 0000-00-00 00:00:00 2023 0 0 7 str. Vol. 24, art. 101526 Dec. 2023 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004 2352-7110 10.1016/j.softx.2023.101526 188330755 1-s2.0-S2352711023002224-main.pdf 1-s2.0-S2352711023002224-main.pdf 1 C4B2FD742CF3C9550D9FF44932BF7C29 d67af5bd7998922422b725c3a7c4b62afbb92d149ff57daa97f1f8422a23cc81 a7096e86-dfac-11ee-a66a-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=181621 https://www.sciencedirect.com/science/article/pii/S2352711023002224 1 57bbbd2f-dfac-11ee-a66a-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=181620 Fakulteta za matematiko in fiziko 0 0 0