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

    5 Downloads (Pure)

    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 publicationSystem Intelligence through Automation & Computing
    Subtitle of host publication2021 26th International Conference on Automation and Computing (ICAC)
    EditorsChenguang Yang
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1-6
    Number of pages6
    ISBN (Electronic)9781860435577
    ISBN (Print)9781665443524
    DOIs
    Publication statusPublished - 15 Nov 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)
    Country/TerritoryUnited Kingdom
    CityPortsmouth
    Period2/09/214/09/21

    Keywords

    • classification
    • ranking
    • preference
    • scoring

    Fingerprint

    Dive into the research topics of 'Data driven transformation of a classification model into ranking'. Together they form a unique fingerprint.

    Cite this