@article {1419,
	title = {The benefits of target relations: A comparison of multitask extensions and classifier chains},
	journal = {Pattern Recognition},
	volume = {107},
	year = {2020},
	pages = {107507},
	abstract = {<p>Multitask (multi-target or multi-output) learning (MTL) deals with simultaneous prediction of several outputs. MTL approaches rely on the optimization of a joint score function over the targets. However, defining a joint score in global models is problematic when the target scales are different. To address such problems, single target (i.e. local) learning strategies are commonly employed. Here we propose alternative tree-based learning strategies to handle the issue with target scaling in global models, and to identify the learning order for chaining operations in local models. In the first proposal, the problems with target scaling are resolved using alternative splitting strategies which consider the learning tasks in a multi-objective optimization framework. The second proposal deals with the problem of ordering in the chaining strategies. We introduce an alternative estimation strategy, minimum error chain policy, that gradually expands the input space using the estimations that approximate to true characteristics of outputs, namely out-of-bag estimations in tree-based ensemble framework. Our experiments on benchmark datasets illustrate the success of the proposed multitask extension of trees compared to the decision trees with de facto design especially for datasets with large number of targets. In line with that, minimum error chain policy improves the performance of the state-of-the-art chaining policies.</p>},
	keywords = {Classifier chains, Ensemble learning, Multi-objective trees, Multitask learning, Stacking},
	issn = {0031-3203},
	doi = {https://doi.org/10.1016/j.patcog.2020.107507},
	url = {https://www.sciencedirect.com/science/article/pii/S0031320320303101},
	author = {Esra Ad{\i}yeke and Mustafa Gokce Baydogan}
}
@article {33,
	title = {A Bag-of-Features Framework to Classify Time Series},
	journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume = {35},
	year = {2013},
	pages = {2796-2802},
	abstract = {<p style="text-align: justify;">Time series classification is an important task with many challenging applications. A nearest neighbor (NN) classifier with dynamic time warping (DTW) distance is a strong solution in this context. On the other hand, feature-based approaches have been proposed as both classifiers and to provide insight into the series, but these approaches have problems handling translations and dilations in local patterns. Considering these shortcomings, we present a framework to classify time series based on a bag-of-features representation (TSBF). Multiple subsequences selected from random locations and of random lengths are partitioned into shorter intervals to capture the local information. Consequently, features computed from these subsequences measure properties at different locations and dilations when viewed from the original series. This provides a feature-based approach that can handle warping (although differently from DTW). Moreover, a supervised learner (that handles mixed data types, different units, etc.) integrates location information into a compact codebook through class probability estimates. Additionally, relevant global features can easily supplement the codebook. TSBF is compared to NN classifiers and other alternatives (bag-of-words strategies, sparse spatial sample kernels, shapelets). Our experimental results show that TSBF provides better results than competitive methods on benchmark datasets from the UCR time series database.</p>},
	keywords = {codebook, feature extraction, supervised learning},
	issn = {0162-8828},
	doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2013.72},
	author = {Mustafa Gokce Baydogan and George Runger and Eugene Tuv}
}
