@article {1602,
	title = {Column generation-based prototype learning for optimizing area under the receiver operating characteristic curve},
	journal = {European Journal of Operational Research},
	volume = {314},
	year = {2024},
	pages = {297{\textendash}307},
	author = {Erhan C Ozcan and Berk G{\"o}rg{\"u}l{\"u} 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 {1421,
	title = {Randomized trees for time series representation and similarity},
	journal = {Pattern Recognition},
	volume = {120},
	year = {2021},
	pages = {108097},
	abstract = {<p>Most of the temporal data mining tasks require representations to capture important characteristics of time series. Representation learning is challenging when time series differ in distributional characteristics and/or show irregularities such as varying lengths and missing observations. Moreover, when time series are multivariate, interactions between variables should be modeled efficiently. This study proposes a unified, flexible time series representation learning framework for both univariate and multivariate time series called Rand-TS. Rand-TS models density characteristics of each time series as a time-varying Gaussian distribution using random decision trees and embeds density information into a sparse vector. Rand-TS can work with time series of various lengths and missing observations, furthermore, it allows using customized features. We illustrate the classification performance of Rand-TS on 113 univariate, 19 multivariate along with 15 univariate time series with varying lengths from UCR database. The results show that in addition to its flexibility, Rand-TS provides competitive classification performance.</p>},
	keywords = {Classification, Random trees, Representation learning, Time series},
	issn = {0031-3203},
	doi = {https://doi.org/10.1016/j.patcog.2021.108097},
	url = {https://www.sciencedirect.com/science/article/pii/S0031320321002843},
	author = {Berk G{\"o}rg{\"u}l{\"u} and Mustafa Gokce Baydogan}
}
