@article {1423,
	title = {An ensemble-based semi-supervised feature ranking for multi-target regression problems},
	journal = {Pattern Recognition Letters},
	volume = {148},
	year = {2021},
	pages = {36-42},
	abstract = {<p>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.</p>},
	keywords = {Feature ranking, Multi-target regression, Semi-supervised learning},
	issn = {0167-8655},
	doi = {https://doi.org/10.1016/j.patrec.2021.04.025},
	url = {https://www.sciencedirect.com/science/article/pii/S0167865521001641},
	author = {Esra Ad{\i}yeke and Mustafa Gokce Baydogan}
}
