Data driven transformation of a classification model into ranking

Salem Chakhar, Yu-ling Lin, Rui Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Given a set of decision objects, an ordinal classifier is an algorithm that can group these objects into preference-ordered decision classes. A ranker is an algorithm that can sort a set of decision objects from highest to lowest, typically using a scoring function. In this paper we propose a ranker to transform the output of ordinal classifier into a ranking using elementary preference information extracted from the dataset. The proposed approach is illustrated using green car data in Taiwan.
Original languageEnglish
Title of host publicationProceedings of the 26th IEEE International Conference on Automation and Computing (ICAC'21), Portsmouth, UK, 2-4 September, 2021
PublisherInstitute of Electrical and Electronics Engineers
Publication statusAccepted for publication - 6 Jun 2021
Event26th IEEE International Conference on Automation and Computing (ICAC'21) - Portsmouth, United Kingdom
Duration: 2 Sep 20214 Sep 2021

Conference

Conference26th IEEE International Conference on Automation and Computing (ICAC'21)
CountryUnited Kingdom
CityPortsmouth
Period2/09/214/09/21

Keywords

  • Classification
  • Ranking
  • Preference
  • Scoring

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