Multiclass Sparse Centroids With Application to Fast Time Series Classification
In this article, we propose an efficient multiclass classification scheme based on sparse centroids classifiers. The proposed strategy exhibits linear complexity with respect to both the number of classes and the cardinality of the feature space. The classifier we introduce is based on binary space partitioning, performed by a decision tree where the assignation law at each node is defined via a sparse centroid classifier. We apply the presented strategy to the time series classification problem, showing by experimental evidence that it achieves performance comparable to that of state-of-the-art methods, but with a significantly lower classification time. The proposed technique can be an effective option in resource-constrained environments where the classification time and the computational cost are critical or, in scenarios, where real-time classification is necessary.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:34 |
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| Enthalten in: |
IEEE transactions on neural networks and learning systems - 34(2023), 8 vom: 01. Aug., Seite 5206-5211 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Bradde, Tommaso [VerfasserIn] |
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| Anmerkungen: |
Date Revised 05.08.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1109/TNNLS.2021.3124300 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM333053389 |
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| 520 | |a In this article, we propose an efficient multiclass classification scheme based on sparse centroids classifiers. The proposed strategy exhibits linear complexity with respect to both the number of classes and the cardinality of the feature space. The classifier we introduce is based on binary space partitioning, performed by a decision tree where the assignation law at each node is defined via a sparse centroid classifier. We apply the presented strategy to the time series classification problem, showing by experimental evidence that it achieves performance comparable to that of state-of-the-art methods, but with a significantly lower classification time. The proposed technique can be an effective option in resource-constrained environments where the classification time and the computational cost are critical or, in scenarios, where real-time classification is necessary | ||
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