@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}
}
@article {1420,
	title = {Explainable boosted linear regression for time series forecasting},
	journal = {Pattern Recognition},
	volume = {120},
	year = {2021},
	pages = {108144},
	abstract = {<p>Time series forecasting involves collecting and analyzing past observations to develop a model to extrapolate such observations into the future. Forecasting of future events is important in many fields to support decision making as it contributes to reducing the future uncertainty. We propose explainable boosted linear regression (EBLR) algorithm for time series forecasting, which is an iterative method that starts with a base model, and explains the model{\textquoteright}s errors through regression trees. At each iteration, the path leading to highest error is added as a new variable to the base model. In this regard, our approach can be considered as an improvement over general time series models since it enables incorporating nonlinear features by residual explanation. More importantly, use of the single rule that contributes to the error most enables access to interpretable results. The proposed approach extends to probabilistic forecasting through generating prediction intervals based on the empirical error distribution. We conduct a detailed numerical study with EBLR and compare against various other approaches. We observe that EBLR substantially improves the base model performance through extracted features, and provide a comparable performance to other well established approaches. The interpretability of the model predictions and high predictive accuracy of EBLR makes it a promising method for time series forecasting.</p>},
	keywords = {ARIMA, Decision trees, Linear regression, Probabilistic forecasting, Time series regression},
	issn = {0031-3203},
	doi = {https://doi.org/10.1016/j.patcog.2021.108144},
	url = {https://www.sciencedirect.com/science/article/pii/S0031320321003319},
	author = {Igor Ilic and Berk G{\"o}rg{\"u}l{\"u} and Mucahit Cevik and Mustafa Gokce Baydogan}
}
@article {1418,
	title = {Estimation of Influence Distance of Bus Stops Using Bus GPS Data and Bus Stop Properties},
	journal = {IEEE Transactions on Intelligent Transportation Systems},
	volume = {20},
	year = {2019},
	pages = {4635-4642},
	doi = {10.1109/TITS.2019.2909645},
	author = {Gokasar, Ilgin and Cetinel, Yigit and Mustafa Gokce Baydogan}
}
