@article {1537,
	title = {Semi-supervised extensions of multi-task tree ensembles},
	journal = {Pattern Recognition},
	volume = {123},
	year = {2022},
	pages = {108393},
	abstract = {<p>Scale inconsistency is a widely encountered issue in multi-output learning problems. Specifically, target sets with multiple real valued or a mixture of categorical and real valued variables require addressing the scale differences to obtain predictive models with sufficiently good performance. Data transformation techniques are often employed to solve that problem. However, these operations are susceptible to different shortcomings such as changing the statistical properties of the data and increase the computational burden. Scale differences also pose problem in semi-supervised learning (SSL) models as they require processing of unsupervised information where distance measures are commonly employed. Classical distance metrics can be criticized as they lose efficiency when variables exhibit type or scale differences, too. Besides, in higher dimensions distance metrics cause problems due to loss of discriminative power. This paper introduces alternative semi-supervised tree-based strategies that are robust to scale differences both in terms of feature and target variables. We propose use of a scale-invariant proximity measure by means of tree-based ensembles to preserve the original characteristics of the data. We update classical tree derivation procedure to a multi-criteria form to resolve scale inconsistencies. We define proximity based clustering indicators and extend the supervised model with unsupervised criteria. Our experiments show that proposed method significantly outperforms its benchmark learning model that is predictive clustering trees.</p>},
	keywords = {Ensemble learning, Multi-objective trees, Multi-task learning, Semi-supervised learning, Totally randomized trees},
	issn = {0031-3203},
	doi = {https://doi.org/10.1016/j.patcog.2021.108393},
	url = {https://www.sciencedirect.com/science/article/pii/S0031320321005549},
	author = {Esra Ad{\i}yeke and Mustafa Gokce Baydogan}
}
@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}
}
