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Multi-operator Decision Trees for Explainable Time-Series Classification

  • Vera ShalaevaEmail author
  • Sami Alkhoury
  • Julien Marinescu
  • Cécile Amblard
  • Gilles Bisson
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 853)

Abstract

Analyzing time-series is a task of rising interest in machine learning. At the same time developing interpretable machine learning tools is the recent challenge proposed by the industry to ease use of these tools by engineers and domain experts. In the paper we address the problem of generating interpretable classification of time-series data. We propose to extend the classical decision tree machine learning algorithm to Multi-operator Temporal Decision Trees (MTDT). The resulting algorithm provides interpretable decisions, thus improving the results readability, while preserving the classification accuracy. Aside MTDT we provide an interactive visualization tool allowing a user to analyse the data, their intrinsic regularities and the learned tree model.

Keywords

Temporal Decision Trees Time-series classification Interpretability 

Notes

Acknowledgments

The study is funded by IKATS project (an Innovative Toolkit for Analysing Time Series), which is a Research and Development project funded by BPIfrance in the frame of the french national PIA program.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Vera Shalaeva
    • 1
    Email author
  • Sami Alkhoury
    • 1
  • Julien Marinescu
    • 1
  • Cécile Amblard
    • 1
  • Gilles Bisson
    • 1
  1. 1.Univ. Grenoble Alpes, CNRS, Grenoble INP, LIGGrenobleFrance

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