Exploring Hierarchical Classification Performance for Time Series Data:
  Dissimilarity Measures and Classifier Comparisons
Celal Alagoz
The comparative performance of hierarchical classification (HC) and flat
classification (FC) methodologies in the realm of time series data analysis is
investigated in this study. Dissimilarity measures, including Jensen-Shannon
Distance (JSD), Task Similarity Distance (TSD), and Classifier Based Distance
(CBD), are leveraged alongside various classifiers such as MINIROCKET, STSF,
and SVM. A subset of datasets from the UCR archive, focusing on multi-class
cases comprising more than two classes, is employed for analysis. A significant
trend is observed wherein HC demonstrates significant superiority over FC when
paired with MINIROCKET utilizing TSD, diverging from conventional
understandings. Conversely, FC exhibits consistent dominance across all
configurations when employing alternative classifiers such as STSF and SVM.
Moreover, TSD is found to consistently outperform both CBD and JSD across
nearly all scenarios, except in instances involving the STSF classifier where
CBD showcases superior performance. This discrepancy underscores the nuanced
nature of dissimilarity measures and emphasizes the importance of their
tailored selection based on the dataset and classifier employed. Valuable
insights into the dynamic interplay between classification methodologies and
dissimilarity measures in the realm of time series data analysis are provided
by these findings. By elucidating the performance variations across different
configurations, a foundation is laid for refining classification methodologies
and dissimilarity measures to optimize performance in diverse analytical
scenarios. Furthermore, the need for continued research aimed at elucidating
the underlying mechanisms driving classification performance in time series
data analysis is underscored, with implications for enhancing predictive
modeling and decision-making in various domains.