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Designing Shapelets for Interpretable Data-Agnostic Classification

Published: 30 July 2021 Publication History

Abstract

Time series shapelets are discriminatory subsequences which are representative of a class, and their similarity to a time series can be used for successfully tackling the time series classification problem. The literature shows that Artificial Intelligence (AI) systems adopting classification models based on time series shapelets can be interpretable, more accurate, and significantly fast. Thus, in order to design a data-agnostic and interpretable classification approach, in this paper we first extend the notion of shapelets to different types of data, i.e., images, tabular and textual data. Then, based on this extended notion of shapelets we propose an interpretable data-agnostic classification method. Since the shapelets discovery can be time consuming, especially for data types more complex than time series, we exploit a notion of prototypes for finding candidate shapelets, and reducing both the time required to find a solution and the variance of shapelets. A wide experimentation on datasets of different types shows that the data-agnostic prototype-based shapelets returned by the proposed method empower an interpretable classification which is also fast, accurate, and stable. In addition, we show and we prove that shapelets can be at the basis of explainable AI methods.

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  • (2024)MemeNet: Toward a Reliable Local Projection for Image Recognition via Semantic FeaturizationIEEE Transactions on Image Processing10.1109/TIP.2024.335933133(1670-1682)Online publication date: 1-Jan-2024
  • (2022)Exploiting auto-encoders for explaining black-box classifiersIntelligenza Artificiale10.3233/IA-22013916:1(115-129)Online publication date: 8-Jul-2022
  • (2022)Explainable AI for Time Series Classification: A Review, Taxonomy and Research DirectionsIEEE Access10.1109/ACCESS.2022.320776510(100700-100724)Online publication date: 2022

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cover image ACM Conferences
AIES '21: Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society
July 2021
1077 pages
ISBN:9781450384735
DOI:10.1145/3461702
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 30 July 2021

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  1. decision support systems
  2. explainable artificial intelligence
  3. interpretable machine learning
  4. transparent classification method

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View all
  • (2024)MemeNet: Toward a Reliable Local Projection for Image Recognition via Semantic FeaturizationIEEE Transactions on Image Processing10.1109/TIP.2024.335933133(1670-1682)Online publication date: 1-Jan-2024
  • (2022)Exploiting auto-encoders for explaining black-box classifiersIntelligenza Artificiale10.3233/IA-22013916:1(115-129)Online publication date: 8-Jul-2022
  • (2022)Explainable AI for Time Series Classification: A Review, Taxonomy and Research DirectionsIEEE Access10.1109/ACCESS.2022.320776510(100700-100724)Online publication date: 2022

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