Data driven transformation of a classification model into ranking

Salem Chakhar; Yu-Ling Lin; Rui Yang

doi:10.23919/ICAC50006.2021.9594062

Data driven transformation of a classification model into ranking

Salem Chakhar, Yu-Ling Lin, Rui Yang

University of Bath

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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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 language	English
Title of host publication	System Intelligence through Automation & Computing
Subtitle of host publication	2021 26th International Conference on Automation and Computing (ICAC)
Editors	Chenguang Yang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781860435577
ISBN (Print)	9781665443524
DOIs	https://doi.org/10.23919/ICAC50006.2021.9594062
Publication status	Published - 15 Nov 2021
Event	26th IEEE International Conference on Automation and Computing (ICAC'21) - Portsmouth, United Kingdom Duration: 2 Sept 2021 → 4 Sept 2021

Conference

Conference	26th IEEE International Conference on Automation and Computing (ICAC'21)
Country/Territory	United Kingdom
City	Portsmouth
Period	2/09/21 → 4/09/21

Keywords

classification
ranking
preference
scoring

Access to Document

10.23919/ICAC50006.2021.9594062

Classi2Ranking
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Accepted author manuscript (Post-print), 215 KB

Cite this

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title = "Data driven transformation of a classification model into ranking",

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.",

keywords = "classification, ranking, preference, scoring",

author = "Salem Chakhar and Yu-Ling Lin and Rui Yang",

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doi = "10.23919/ICAC50006.2021.9594062",

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booktitle = "System Intelligence through Automation & Computing",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "26th IEEE International Conference on Automation and Computing (ICAC'21) ; Conference date: 02-09-2021 Through 04-09-2021",

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Chakhar, S, Lin, Y-L & Yang, R 2021, Data driven transformation of a classification model into ranking. in C Yang (ed.), System Intelligence through Automation & Computing: 2021 26th International Conference on Automation and Computing (ICAC). Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 26th IEEE International Conference on Automation and Computing (ICAC'21), Portsmouth, United Kingdom, 2/09/21. https://doi.org/10.23919/ICAC50006.2021.9594062

Data driven transformation of a classification model into ranking. / Chakhar, Salem; Lin, Yu-Ling; Yang, Rui.
System Intelligence through Automation & Computing: 2021 26th International Conference on Automation and Computing (ICAC). ed. / Chenguang Yang. Institute of Electrical and Electronics Engineers Inc., 2021. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Data driven transformation of a classification model into ranking

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AU - Lin, Yu-Ling

AU - Yang, Rui

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AB - 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.

KW - classification

KW - ranking

KW - preference

KW - scoring

U2 - 10.23919/ICAC50006.2021.9594062

DO - 10.23919/ICAC50006.2021.9594062

M3 - Conference contribution

SN - 9781665443524

SP - 1

EP - 6

BT - System Intelligence through Automation & Computing

A2 - Yang, Chenguang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 26th IEEE International Conference on Automation and Computing (ICAC'21)

Y2 - 2 September 2021 through 4 September 2021

ER -

Data driven transformation of a classification model into ranking

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Keywords

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