{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T17:26:52Z","timestamp":1754155612283,"version":"3.41.2"},"reference-count":22,"publisher":"Emerald","issue":"8","license":[{"start":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T00:00:00Z","timestamp":1524528000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["K"],"published-print":{"date-parts":[[2018,9,26]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This paper aims to learn a decision-maker\u2019s (DM\u2019s) decision model that is characterized in terms of the attitudinal character and the attributes weight vector, both of which are specific to the DM. The authors take the learning information in the form of the exemplary preferences, given by a DM. The learning approach is formalized by bringing together the recent research in the choice models and machine learning. The study is validated on a set of 12 benchmark data sets.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>The study includes emerging preference learning algorithms.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Learning of a DM\u2019s attitudinal choice model.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>Preferences-based learning of a DM\u2019s attitudinal decision model.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/k-10-2017-0396","type":"journal-article","created":{"date-parts":[[2018,4,24]],"date-time":"2018-04-24T06:54:15Z","timestamp":1524552855000},"page":"1569-1584","source":"Crossref","is-referenced-by-count":0,"title":["Learning attitudinal decision model through pair-wise preferences"],"prefix":"10.1108","volume":"47","author":[{"given":"Manish","family":"Aggarwal","sequence":"first","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2018,4,24]]},"reference":[{"key":"key2021041509184607900_ref001","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.asoc.2014.09.049","article-title":"Compensative weighted averaging aggregation operators","volume":"28","year":"2015","journal-title":"Applied Soft Computing"},{"year":"2018","article-title":"On the class of attitudinal multinomial logit models, to be published","key":"key2021041509184607900_ref002"},{"issue":"3\/4","key":"key2021041509184607900_ref003","article-title":"Preference-based learning of ideal solutions in TOPSIS-like decision models","volume":"22","year":"2014","journal-title":"Journal of Multi-Criteria Decision Analysis"},{"year":"2017","article-title":"Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening","key":"key2021041509184607900_ref004"},{"issue":"1","key":"key2021041509184607900_ref005","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.fss.2010.08.011","article-title":"Citation-based journal ranks: the use of fuzzy measures","volume":"167","year":"2011","journal-title":"Fuzzy Sets and Systems"},{"key":"key2021041509184607900_ref006","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1007\/s005210050025","article-title":"Applications of mlp networks to bond rating and house pricing","volume":"8","year":"1999","journal-title":"Neural Computation and Applications"},{"unstructured":"Frank, A. and Asuncion, A. 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