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

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

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.

• Language: English
• Keywords: machine learning, multi-target regression, multi-label classification, decision trees, feature importance, semi-supervised learning, random forests
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FMF - Faculty of Mathematics and Physics
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 7 str.
• Numbering: Vol. 24, art. 101526
• PID: 20.500.12556/RUL-154950
• UDC: 004
• ISSN on article: 2352-7110
• DOI: 10.1016/j.softx.2023.101526
• COBISS.SI-ID: 188330755
• Publication date in RUL: 11.03.2024

Record is a part of a journal

• Title: SoftwareX
• Publisher: Elsevier
• ISSN: 2352-7110
• COBISS.SI-ID: 526120473

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: strojno učenje, klasifikacija, označevanje

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0103
• Name: Tehnologije znanja

• Funder: EC - European Commission
• Funding programme: FP7
• Project number: 612944
• Name: Learning from Massive, Incompletely annotated, and Structured Data
• Acronym: MAESTRA