{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T07:32:56Z","timestamp":1766820776373,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T00:00:00Z","timestamp":1682294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union","award":["101076911"],"award-info":[{"award-number":["101076911"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Pattern mining is a valuable tool for exploratory data analysis, but identifying relevant patterns for a specific user is challenging. Various interestingness measures have been developed to evaluate patterns, but they may not efficiently estimate user-specific functions. Learning user-specific functions by ranking patterns has been proposed, but this requires significant time and training samples. In this paper, we present a solution that formulates the problem of learning pattern ranking functions as a multi-criteria decision-making problem. Our approach uses an analytic hierarchy process (AHP) to elicit weights for different interestingness measures based on user preference. We also propose an active learning mode with a sensitivity-based heuristic to minimize user ranking queries while still providing high-quality results. Experiments show that our approach significantly reduces running time and returns precise pattern ranking while being robust to user mistakes, compared to state-of-the-art approaches.<\/jats:p>","DOI":"10.3390\/a16050218","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T01:37:01Z","timestamp":1682386621000},"page":"218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Boosting the Learning for Ranking Patterns"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3436-0154","authenticated-orcid":false,"given":"Nassim","family":"Belmecheri","sequence":"first","affiliation":[{"name":"Laboratoire d\u2019Informatique et des Technologies de l\u2019Information d\u2019Oran, Universit\u00e9 Oran1, Oran 31000, Algeria"},{"name":"Simula Research Laboratory, 0164 Oslo, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3204-6065","authenticated-orcid":false,"given":"Noureddine","family":"Aribi","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Informatique et des Technologies de l\u2019Information d\u2019Oran, Universit\u00e9 Oran1, Oran 31000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2524-9462","authenticated-orcid":false,"given":"Nadjib","family":"Lazaar","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Informatique, de Robotique et de Micro\u00e9lectronique de Montpellier, Universit\u00e9 de Montpellier, CNRS, 34000 Montpellier, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4736-2976","authenticated-orcid":false,"given":"Yahia","family":"Lebbah","sequence":"additional","affiliation":[{"name":"Laboratoire d\u2019Informatique et des Technologies de l\u2019Information d\u2019Oran, Universit\u00e9 Oran1, Oran 31000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6245-7661","authenticated-orcid":false,"given":"Samir","family":"Loudni","sequence":"additional","affiliation":[{"name":"Laboratoire des Sciences du Num\u00e9rique de Nantes, IMT Atlantique, 44307 Nantes, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,24]]},"reference":[{"key":"ref_1","unstructured":"Silberschatz, A., and Tuzhilin, A. 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