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J48SS: A Novel Decision Tree Approach for the Handling of Sequential and Time Series Data

1
Department of Mathematics, Computer Science and Physics, University of Udine, Via delle Scienze, 206, 33100 Udine, Italy
2
R&D Deparment, Gap S.r.l.u., Via Tricesimo, 246, 33100 Udine, Italy
3
Department of Mathematics and Computer Science, University of Ferrara, Via Giuseppe Saragat, 1, 44122 Ferrara, Italy
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper J48S: A Sequence Classification Approach to Text Analysis Based on Decision Trees, published in the 24th International Conference on Information and Software Technologies (ICIST), Vilnius, Lithuania, 4–6 October 2018.
Computers 2019, 8(1), 21; https://doi.org/10.3390/computers8010021
Received: 11 February 2019 / Revised: 25 February 2019 / Accepted: 27 February 2019 / Published: 5 March 2019
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Abstract

Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracking the evolution of customer behaviors over time. Alternatively, it can be represented by time series, as in the stock market to characterize price histories. In some analysis tasks, temporal information is complemented by other kinds of data, which may be represented by static attributes, e.g., categorical or numerical ones. This paper presents J48SS, a novel decision tree inducer capable of natively mixing static (i.e., numerical and categorical), sequential, and time series data for classification purposes. The novel algorithm is based on the popular C4.5 decision tree learner, and it relies on the concepts of frequent pattern extraction and time series shapelet generation. The algorithm is evaluated on a text classification task in a real business setting, as well as on a selection of public UCR time series datasets. Results show that it is capable of providing competitive classification performances, while generating highly interpretable models and effectively reducing the data preparation effort. View Full-Text
Keywords: machine learning; decision trees; sequential data; pattern mining; time series classification; evolutionary algorithms machine learning; decision trees; sequential data; pattern mining; time series classification; evolutionary algorithms
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Brunello, A.; Marzano, E.; Montanari, A.; Sciavicco, G. J48SS: A Novel Decision Tree Approach for the Handling of Sequential and Time Series Data. Computers 2019, 8, 21.

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