Incremental predictive clustering trees for online semi-supervised multi-target regression
| العنوان: | Incremental predictive clustering trees for online semi-supervised multi-target regression |
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| المؤلفون: | Sašo Džeroski, Panče Panov, Aljaž Osojnik |
| المصدر: | Machine Learning |
| بيانات النشر: | Springer Science and Business Media LLC, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Computer science, business.industry, 02 engineering and technology, Machine learning, computer.software_genre, Oracle, Regression, Task (project management), Tree (data structure), ComputingMethodologies_PATTERNRECOGNITION, Multi target, Artificial Intelligence, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Cluster analysis, business, computer, Online setting, Software |
| الوصف: | In many application settings, labeling data examples is a costly endeavor, while unlabeled examples are abundant and cheap to produce. Labeling examples can be particularly problematic in an online setting, where there can be arbitrarily many examples that arrive at high frequencies. It is also problematic when we need to predict complex values (e.g., multiple real values), a task that has started receiving considerable attention, but mostly in the batch setting. In this paper, we propose a method for online semi-supervised multi-target regression. It is based on incremental trees for multi-target regression and the predictive clustering framework. Furthermore, it utilizes unlabeled examples to improve its predictive performance as compared to using just the labeled examples. We compare the proposed iSOUP-PCT method with supervised tree methods, which do not use unlabeled examples, and to an oracle method, which uses unlabeled examples as though they were labeled. Additionally, we compare the proposed method to the available state-of-the-art methods. The method achieves good predictive performance on account of increased consumption of computational resources as compared to its supervised variant. The proposed method also beats the state-of-the-art in the case of very few labeled examples in terms of performance, while achieving comparable performance when the labeled examples are more common. |
| تدمد: | 1573-0565 0885-6125 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd3c25f765440603274f4746774a5358 https://doi.org/10.1007/s10994-020-05918-z |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....bd3c25f765440603274f4746774a5358 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 15730565 08856125 |
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