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4308. Optimal Shapelets Tree for Time Series Interpretable Classification
Invited abstract in session MC-26: Combinatorial Optimization for Machine Learning, stream Combinatorial Optimization.
Monday, 12:30-14:00Room: 012 (building: 208)
Authors (first author is the speaker)
| 1. | Stefano Gualandi
|
| Pavia, Department of Mathematics, "Felice Casorati" | |
| 2. | Lorenzo Bonasera
|
| Dipartimento di Matematica "Felice Casorati", Università degli Studi di Pavia |
Abstract
Time series shapelets are a state-of-the-art data mining technique for supervised classification tasks in time series domains. Shapelets are time series subsequences that retain the most discriminating power in time series. The main advantage of shapelet-based methods is their great interpretability. We propose a novel Mixed-Integer Programming model to optimize shapelets discovery based on optimal binary decision trees. Computational results for a large class of datasets show that our approach achieves performance comparable with state-of-the-art shapelets-based classification methods. To the best of our knowledge, this is the first approach based on optimal decision tree induction for time series classification.
Keywords
- Machine Learning
- Programming, Mixed-Integer
- Optimization Modeling
Status: accepted
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