Construction and refinement of preference ordered decision classes

Hoang Nhat Dau, Salem Chakhar, Djamila Ouelhadj, Ahmed Abubahia

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Abstract

Preference learning methods are commonly used in multicriteria analysis. The working principle of these methods is similar to classical machine learning techniques. A common issue to both machine learning and preference learning methods is the difficulty of the definition of decision classes and the assignment of objects to these classes, especially for large datasets. This paper proposes two procedures permitting to automatize the construction of decision classes. It also proposes two simple refinement procedures, that rely on the 80-20 principle, permitting to map the output of the construction procedures into a manageable set of decision classes. The proposed construction procedures rely on the most elementary preference relation, namely dominance relation, which avoids the need for additional information or distance/(di)similarity functions, as with most of existing clustering methods. Furthermore, the simplicity of the 80-20 principle on which the refinement procedures are based, make them very adequate to large datasets. Proposed procedures are illustrated and validated using real-world datasets.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationContributions Presented at the 19th UK Workshop on Computational Intelligence, September 4-6, 2019, Portsmouth, UK
EditorsZhaojie Ju, Longzhi Yang, Chenguang Yang, Alexander Gegov, Dalin Zhou
Place of PublicationCham, Switzerland
PublisherSpringer International Publishing
Pages248-261
Number of pages14
Volume1043
Edition1
ISBN (Electronic)78-3-030-29933-0
ISBN (Print)978-3-030-29932-3
DOIs
Publication statusPublished - Jan 2020
Event19th UK Workshop on Computational Intelligence - Portsmouth, United Kingdom
Duration: 4 Sep 20195 Sep 2019
Conference number: 19
https://www.ukci2019.port.ac.uk/

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
Volume1043
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Workshop

Workshop19th UK Workshop on Computational Intelligence
Abbreviated titleUKCI 2019
Country/TerritoryUnited Kingdom
CityPortsmouth
Period4/09/195/09/19
OtherThe UKCI 2019 covers both theory and applications in computational intelligence. The topics of interest include
Fuzzy Systems
Neural Networks
Evolutionary Computation
Evolving Systems
Machine Learning
Data Mining
Cognitive Computing
Intelligent Robotics
Hybrid Methods
Deep Learning
Applications of Computational Intelligence
Internet address

Keywords

  • Preference learning
  • Classification
  • Clustering
  • Classes Construction

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