@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}
}
@conference {1167,
	title = {A scale-space theory and bag-of-features based time series classification method},
	booktitle = {Signal Processing and Communications Applications Conference (SIU), 2017 25th},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Altay, Tayip and Mustafa Gokce Baydogan}
}
@article {35,
	title = {SMT: Sparse multivariate tree},
	journal = {Statistical Analysis and Data Mining},
	volume = {7},
	year = {2014},
	month = {02/2014},
	pages = {53-69},
	abstract = {<p style="text-align: justify;">A multivariate decision tree attempts to improve upon the single variable split in a traditional tree. With the increase in datasets with many features and a small number of labeled instances in a variety of domains (bioinformatics, text mining, etc.), a traditional tree-based approach with a greedy variable selection at a node may omit important information. Therefore, the recursive partitioning idea of a simple decision tree combined with the intrinsic feature selection of L1 regularized logistic regression (LR) at each node is a natural choice for a multivariate tree model that is simple, but broadly applicable. This natural solution leads to the sparse multivariate tree (SMT) considered here. SMT can naturally handle non-time-series data and is extended to handle time-series classification problems with the power of extracting interpretable temporal patterns (e.g., means, slopes, and deviations). Binary L1 regularized LR models are used here for binary classification problems. However, SMT may be extended to solve multiclass problems with multinomial LR models. The accuracy and computational efficiency of SMT is compared to a large number of competitors on time series and non-time-series data.</p>},
	keywords = {decision tree, feature extraction, fused Lasso, Lasso, time series classification},
	issn = {1932-1872},
	doi = {10.1002/sam.11208},
	url = {http://dx.doi.org/10.1002/sam.11208},
	author = {Houtao Deng and Mustafa Gokce Baydogan and George Runger}
}
