Studying the effect of different Lp norms in the context of Time Series Ordinal Classification
David Guijo-Rubio 
Víctor Manuel Vargas-Yun 
Pedro Antonio Gutiérrez 
César Hervás-Martínez Proceedings of the XIX conference of the spanish association for artificial intelligence (CAEPIA)
Abstract
Time Series Ordinal Classification (TSOC) is yet an unexplored field of machine learning consisting in the classification of time series whose labels follow a natural order relationship between them. In this context, a well-known approach for time series nominal classification was previously used: the Shapelet Transform (ST). The exploitation of the ordinal information was included in two steps of the ST algorithm: 1) by using the Pearson's determination coefficient (R²) for computing the quality of the shapelets, which favours shapelets with better ordering, and 2) by applying an ordinal classifier instead of a nominal one to the transformed dataset. For this, the distance between labels was represented by the absolute value of the difference between the corresponding ranks, i.e. by the L1 norm. In this paper, we study the behaviour of different Lp norms for representing class distances in ordinal regression, evaluating 9 different Lp norms with 7 ordinal time series datasets from the UEA-UCR time series classification repository and 10 different ordinal classifiers. The results achieved demonstrate that the Pearson's determination coefficient using the L1.9 norm in the computation of the difference between the shapelet and the time series labels achieves a significantly better performance when compared to the rest of the approaches, in terms of both Correct Classification Rate (CCR) and Average Mean Absolute Error (AMAE).
Keywords
BibTex Citation
@inproceedings{Guijo2021Studying,
author = {Guijo-Rubio, David and Vargas-Yun, V{\' i}ctor Manuel and Guti{\' e}rrez, Pedro Antonio and Herv{\' a}s-Mart{\' i}nez, C{\' e}sar},
booktitle = {Proceedings of the {XIX} conference of the spanish association for artificial intelligence ({CAEPIA})},
doi = {10.1007/978-3-030-85713-4_5},
year = {2021},
pages = {44--53},
organization = {Springer},
title = {Studying the effect of different {Lp} norms in the context of {Time} {Series} {Ordinal} {Classification}},
url = {https://doi.org/10.1007/978-3-030-85713-4_5},
volume = {12882},
}
BibTex Unicode Citation
@inproceedings{Guijo2021Studying,
author = {Guijo-Rubio, David and Vargas-Yun, Víctor Manuel and Gutiérrez, Pedro Antonio and Hervás-Martínez, César},
booktitle = {Proceedings of the {XIX} conference of the spanish association for artificial intelligence ({CAEPIA})},
doi = {10.1007/978-3-030-85713-4_5},
year = {2021},
pages = {44--53},
organization = {Springer},
title = {Studying the effect of different {Lp} norms in the context of {Time} {Series} {Ordinal} {Classification}},
url = {https://doi.org/10.1007/978-3-030-85713-4_5},
volume = {12882},
}
APA Citation
Guijo-Rubio, D., Vargas-Yun, V. M., Gutiérrez, P. A., & Hervás-Martínez, C. (2021). Studying the effect of different Lp norms in the context of Time Series Ordinal Classification. Proceedings of the XIX Conference of the Spanish Association for Artificial Intelligence (CAEPIA), 12882, 44–53. https://doi.org/10.1007/978-3-030-85713-4_5
RIS Citation
TY - CONF
AU - Guijo-Rubio, David
AU - Vargas-Yun, Víctor Manuel
AU - Gutiérrez, Pedro Antonio
AU - Hervás-Martínez, César
C3 - Proceedings of the XIX conference of the spanish association for artif
icial intelligence (CAEPIA)
DA - 2021///
C2 - 2021
DO - 10.1007/978-3-030-85713-4_5
ID - temp_id_193685003993
PB - Springer
SP - 44-53
TI - Studying the effect of different Lp norms in the context of Time Serie
s Ordinal Classification
UR - https://doi.org/10.1007/978-3-030-85713-4_5
VL - 12882
ER -
CV Citation
D. Guijo-Rubio, V.M. Vargas-Yun (CA), P.A. Gutiérrez, C. Hervás-Martínez (2/4). "Studying the effect of different Lp norms in the context of Time Series Ordinal Classification". Proceedings of the XIX conference of the spanish association for artificial intelligence (CAEPIA), pp. 44-53, 2021.