{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T02:46:17Z","timestamp":1782960377092,"version":"3.54.5"},"reference-count":88,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T00:00:00Z","timestamp":1739145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>A new approach to the local and global explanation based on selecting a convex hull constructed for the finite number of points around an explained instance is proposed. The convex hull allows us to consider a dual representation of instances in the form of convex combinations of extreme points of a produced polytope. Instead of perturbing new instances in the Euclidean feature space, vectors of convex combination coefficients are uniformly generated from the unit simplex, and they form a new dual dataset. A dual linear surrogate model is trained on the dual dataset. The explanation feature importance values are computed by means of simple matrix calculations. The approach can be regarded as a modification of the well-known model LIME. The dual representation inherently allows us to get the example-based explanation. The neural additive model is also considered as a tool for implementing the example-based explanation approach. Many numerical experiments with real datasets are performed for studying the approach. A code of proposed algorithms is available. The proposed results are fundamental and can be used in various application areas. They do not involve specific human subjects and human data.<\/jats:p>","DOI":"10.3389\/frai.2025.1506074","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T06:49:57Z","timestamp":1739170197000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Dual feature-based and example-based explanation methods"],"prefix":"10.3389","volume":"8","author":[{"given":"Andrei","family":"Konstantinov","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boris","family":"Kozlov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stanislav","family":"Kirpichenko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lev","family":"Utkin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vladimir","family":"Muliukha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/FUZZ-IEEE.2019.8858846","article-title":"\u201cLEAFAGE: example-based and feature importance-based explanations for black-box ML models,\u201d","volume-title":"2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","author":"Adhikari","year":"2019"},{"key":"B3","first-page":"4699","article-title":"\u201cNeural additive models: interpretable machine learning with neural nets,\u201d","volume-title":"Advances in Neural Information Processing Systems","author":"Agarwal","year":"2021"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1111\/rssb.12377","article-title":"Visualizing the effects of predictor variables in black box supervised learning models","volume":"82","author":"Apley","year":"2020","journal-title":"J. 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