@article {1422,
	title = {A new feature-based time series classification method by using scale-space extrema},
	journal = {Engineering Science and Technology, an International Journal},
	year = {2021},
	abstract = {<p>Time series data mining has received significant attention over the past decade, and many approaches have focused on classification tasks where the goal is to define the label of a test time series, given labeled training data. Time series classification approaches can be broadly grouped into two categories as instance-based and feature-based methods. Instance-based approaches utilize similarity information in a nearest-neighbor setting to classify time series data. Although approaches from this category provide accurate results, their performance degrades with long and noisy time series. On the other hand, feature-based approaches extract features to deal with the limitations of instance-based approaches; however, these approaches work with predefined features and may not be successful in certain classification problems. This study proposes a time series classification approach that benefits from both scale-space theory and bag-of-features technique. The method starts with finding the scale-space extrema points (i.e. key points) of each time series according to the SiZer (SIgnificant ZERo crossings of the derivatives) method, and then proceeds to create the local features set around these points. After extraction of the local features from each key point, a bag-of-features representation for each time series is constructed as the summary of the key point characteristics. We evaluate the success of the proposed representation on time series classification problems from various domains. Our experimental results show that our proposal provides competitive results compared to widely used approaches in the literature.</p>},
	keywords = {Bag-of-Features Technique, Feature-based Classification, Scale-Space Theory, SiZer, time series classification},
	issn = {2215-0986},
	doi = {https://doi.org/10.1016/j.jestch.2021.03.017},
	url = {https://www.sciencedirect.com/science/article/pii/S2215098621000823},
	author = {Tayip Altay 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}
}
