Multidimensional curve classification using passing—through regions
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Pattern Extraction for Time Series Classification
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
Clustering short time series gene expression data
Bioinformatics
IEICE - Transactions on Information and Systems
Decision Trees for Functional Variables
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Boosting interval based literals
Intelligent Data Analysis
Adaptive clustering for time series: Application for identifying cell cycle expressed genes
Computational Statistics & Data Analysis
A periodogram-based metric for time series classification
Computational Statistics & Data Analysis
Bioinformatics
Segment and combine approach for non-parametric time-series classification
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Possibilistic nonlinear dynamical analysis for pattern recognition
Pattern Recognition
DCE-MRI and DWI integration for breast lesions assessment and heterogeneity quantification
Journal of Biomedical Imaging - Special issue on Advanced Signal Processing Methods for Biomedical Imaging
A Domain Knowledge as a Tool For Improving Classifiers
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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This paper proposes an extension of classification trees to time series input variables. A new split criterion based on time series proximities is introduced. First, the criterion relies on an adaptive (i.e., parameterized) time series metric to cover both behaviors and values proximities. The metrics parameters may change from one internal node to another to achieve the best bisection of the set of time series. Second, the criterion involves the automatic extraction of the most discriminating subsequences. The proposed time series classification tree is applied to a wide range of datasets: public and new, real and synthetic, univariate and multivariate data. We show, through the experiments performed in this study, that the proposed tree outperforms temporal trees using standard time series distances and performs well compared to other competitive time series classifiers.