| Title: | Variational oblique predictive clustering trees |
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| Authors: | ID Andonovikj, Viktor, Institut "Jožef Stefan" (Author)
ID Džeroski, Sašo, Institut "Jožef Stefan" (Author)
ID Boshkoska, Biljana Mileva, Institut "Jožef Stefan" (Author)
ID Boškoski, Pavle, Institut "Jožef Stefan" (Author) |
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| Files: |  URL - Source URL, visit https://www.sciencedirect.com/science/article/pii/S0957417426001685
 PDF - Presentation file, download (2,20 MB)
MD5: B7052A9221ABA62354203C8B8EAD7BE2
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| Language: | English |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: |  IJS - Jožef Stefan Institute
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| Abstract: | Oblique predictive clustering trees (SPYCTs) are semi-supervised multi-target prediction models mainly used for structured output prediction (SOP) problems. They are computationally efficient and when combined in ensembles they achieve state-of-the-art results. However, one major issue is that it is challenging to interpret an ensemble of SPYCTs without the use of a model-agnostic method. We propose variational oblique predictive clustering trees, which address this challenge. The parameters of each split node are treated as random variables, described with a probability distribution, and they are learned through the Variational Bayes method. We evaluate the model on several benchmark datasets of different sizes. The experimental analyses show that a single variational oblique predictive clustering tree (VSPYCT) achieves competitive, and sometimes better predictive performance than the ensemble of standard SPYCTs. We also present a method for extracting feature importance scores from the model. Finally, we present a method to visually interpret the model’s decision making process through analysis of the relative feature importance in each split node. |
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| Keywords: | machine learning, predictive clustering, interpretable models, structured output prediction, uncertainty quantification |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 29.04.2025 |
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| Article acceptance date: | 15.01.2026 |
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| Publication date: | 22.01.2026 |
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| Publisher: | Elsevier |
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| Year of publishing: | 2026 |
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| Number of pages: | str. 1-31 |
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| Numbering: | Vol. [article no.] 131255 |
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| Source: | Nizozemska |
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| PID: | 20.500.12556/DiRROS-27644  |
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| UDC: | 004.8 |
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| ISSN on article: | 1873-6793 |
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| DOI: | 10.1016/j.eswa.2026.131255 |
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| COBISS.SI-ID: | 266512899 |
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| Copyright: | © 2026 The Author(s). |
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| Note: | Nasl. z nasl. zaslona;
Soavtorji: Sašo Džeroski, Biljana Mileva Boshkoska, Pavle Boškoski;
Opis vira z dne 28. 1. 2026;
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| Publication date in DiRROS: | 17.02.2026 |
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| Views: | 205 |
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| Downloads: | 64 |
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