CLUSPLUS : a decision tree-based framework for predicting structured outputs

Petković, Matej (avtor), Levatić, Jurica (avtor), Kocev, Dragi (avtor), Breskvar, Martin (avtor), Džeroski, Sašo (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: machine learning, multi-target regression, multi-label classification, decision trees, feature importance, semi-supervised learning, random forests
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FMF - Fakulteta za matematiko in fiziko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2023
• Št. strani: 7 str.
• Številčenje: Vol. 24, art. 101526
• PID: 20.500.12556/RUL-154950
• UDK: 004
• ISSN pri članku: 2352-7110
• DOI: 10.1016/j.softx.2023.101526
• COBISS.SI-ID: 188330755
• Datum objave v RUL: 11.03.2024

Gradivo je del revije

• Naslov: SoftwareX
• Založnik: Elsevier
• ISSN: 2352-7110
• COBISS.SI-ID: 526120473

Licence

• Licenca: CC BY-NC-ND 4.0, Creative Commons Priznanje avtorstva-Nekomercialno-Brez predelav 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by-nc-nd/4.0/deed.sl
• Opis: Najbolj omejujoča licenca Creative Commons. Uporabniki lahko prenesejo in delijo delo v nekomercialne namene in ga ne smejo uporabiti za nobene druge namene.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: strojno učenje, klasifikacija, označevanje

Projekti

• Financer: ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
• Številka projekta: P2-0103
• Naslov: Tehnologije znanja

• Financer: EC - European Commission
• Program financ.: FP7
• Številka projekta: 612944
• Naslov: Learning from Massive, Incompletely annotated, and Structured Data
• Akronim: MAESTRA