{"type":"ScholarlyArticle","title":"INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification","authors":[{"name":"Seongjun Lee","slug":"seongjun-lee","given":"Seongjun","family":"Lee","affiliation":""},{"name":"Seokhyun Lee","slug":"seokhyun-lee","given":"Seokhyun","family":"Lee","affiliation":""},{"name":"Changhee Lee","slug":"changhee-lee","given":"Changhee","family":"Lee","affiliation":""}],"abstract":"Discovering shapelets -- i.e., discriminative temporal patterns within time series -- has been widely studied to address the inherent complexity of time-series classification (TSC) and to make model decision-making processes more transparent. \r\nHowever, existing methods primarily focus on population-level shapelets optimized across the entire dataset, which leads to two fundamental limitations: (i) population-level patterns often misalign with instance-specific features, resulting in suboptimal performance and potentially misleading interpretations, and (ii) most methods treat shapelets as independent entities, overlooking important temporal dependencies and interactions among multiple patterns.\r\nTo address these limitations, we propose INSHAPE, an interpretable TSC framework that discovers variable-length, discriminative temporal patterns specific to each time series.\r\nINSHAPE identifies these patterns as non-overlapping segments and models their temporal dependencies, thereby providing clear instance-level interpretations while achieving strong predictive performance. \r\nFurthermore, INSHAPE bridges local and global interpretability through a bottom-up approach, aggregating instance-level shapelets into prototypical (population-level) shapelets. \r\nExtensive experiments on 128 UCR and 30 UEA benchmark datasets show that INSHAPE consistently outperforms state-of-the-art shapelet-based methods while providing more intuitive and interpretable insights.","doi":"10.24963/ijcai.2026/496","url":"https://ijcai.org/proceedings/2026/496","pdf":"https://ijcai.org/proceedings/2026/0496.pdf","markdown":"https://ijcai.org/proceedings/2026/496.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/496","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":496,"pages":{"first":4456,"last":4464},"keywords":["Machine Learning: Feature extraction, selection and dimensionality reduction","Machine Learning: Classification","Machine Learning: Time series and data streams","Machine Learning: Explainable/Interpretable machine learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}