{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:44:45Z","timestamp":1781279085755,"version":"3.54.1"},"reference-count":78,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T00:00:00Z","timestamp":1664409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Interactive machine learning (IML) enables the incorporation of human expertise because the human participates in the construction of the learned model. Moreover, with human-in-the-loop machine learning (HITL-ML), the human experts drive the learning, and they can steer the learning objective not only for accuracy but perhaps for characterisation and discrimination rules, where separating one class from others is the primary objective. Moreover, this interaction enables humans to explore and gain insights into the dataset as well as validate the learned models. Validation requires transparency and interpretable classifiers. The huge relevance of understandable classification has been recently emphasised for many applications under the banner of explainable artificial intelligence (XAI). We use parallel coordinates to deploy an IML system that enables the visualisation of decision tree classifiers but also the generation of interpretable splits beyond parallel axis splits. Moreover, we show that characterisation and discrimination rules are also well communicated using parallel coordinates. In particular, we report results from the largest usability study of a IML system, confirming the merits of our approach.<\/jats:p>","DOI":"10.3390\/info13100464","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T04:09:36Z","timestamp":1664424576000},"page":"464","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Constructing Explainable Classifiers from the Start\u2014Enabling Human-in-the Loop Machine Learning"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7775-0780","authenticated-orcid":false,"given":"Vladimir","family":"Estivill-Castro","sequence":"first","affiliation":[{"name":"Department of Information and Communications Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eugene","family":"Gilmore","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Brisbane 4111, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9668-849X","authenticated-orcid":false,"given":"Ren\u00e9","family":"Hexel","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Brisbane 4111, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1016\/S1071-5819(03)00038-7","article-title":"The role of trust in automation reliance","volume":"58","author":"Dzindolet","year":"2003","journal-title":"Int. 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