An ensemble-based semi-supervised feature ranking for multi-target regression problems
Pattern Recognition Letters, cilt.148, ss.36-42, 2021 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 148
- Basım Tarihi: 2021
- Doi Numarası: 10.1016/j.patrec.2021.04.025
- Dergi Adı: Pattern Recognition Letters
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Computer & Applied Sciences, INSPEC, zbMATH
- Sayfa Sayıları: ss.36-42
- Anahtar Kelimeler: Semi-supervised learning, Feature ranking, Multi-target regression
- Boğaziçi Üniversitesi Adresli: Evet
Özet
This study focuses on semi-supervised feature ranking (FR) applications for multi-target regression problems (MTR). As MTRs require prediction of several targets, we use a learning model that includes target interrelations via multi-objective trees. In processing the features for a semi-supervised learning model, transformation or scaling operations are usually required. To resolve this issue, we create a dissimilarity matrix via totally randomized trees to process the unsupervised information. Besides, we treat the split score function as a vector to make it suitable for considering each criterion regardless of their scales. We propose a semi-supervised FR scheme embedded to multi-objective trees that takes into account target and feature contributions simultaneously. Proposed FR score is compared with the state-of-the-art multi-target FR strategies via statistical analyses. Experimental studies show that proposed score significantly improves the performance of a recent tree-based and competitive multi-target learning model, i.e. predictive clustering trees. In addition, proposed approach outperforms its benchmarks when the available labelled data increase.