Domination dependency analysis of sales marketing based on multi-label classification using label ordering and cycle chain classification

Boonyarit Soonsiripanichkul, Tomohiro Murata

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

Abstract

Multi-label classification (MLC) is the technique to solve conditional problem where decisions is a set of labels. Classification is the learning task by using historical examples to make model of conditions to make decision of unseen example. To improve decision in MLC. We can use advantage of domination analysis or dependency relations between the classes by using enhanced Bayesian Chain Classifier (BCC). We introduce an approach for chaining classifier primary order by its individual label accuracy priority (LPC-CC). Our method considers the dependencies among label based on label accuracy priority ordering. Thus, Binary Relevance (BR) theoretical is used for label sequencing priority, and cycle classifiers chain using nave Bayes is for finding domination. The model have been tested on 2 well-known benchmark datasets named Yeast and Emotions and a collection of car sell records from a Thailand Automotive Company.

Original languageEnglish
Title of host publicationProceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1048-1053
Number of pages6
ISBN (Electronic)9781467389853
DOIs
Publication statusPublished - 2016 Aug 31
Event5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 - Kumamoto, Japan
Duration: 2016 Jul 102016 Jul 14

Other

Other5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016
CountryJapan
CityKumamoto
Period16/7/1016/7/14

Fingerprint

Labels
Marketing
Sales
Classifiers
Yeast
Railroad cars
Industry

Keywords

  • Chain Classifier
  • Domination Analysis
  • Label Dependency
  • Label Ordering
  • Problem Transformation

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Soonsiripanichkul, B., & Murata, T. (2016). Domination dependency analysis of sales marketing based on multi-label classification using label ordering and cycle chain classification. In Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016 (pp. 1048-1053). [7557768] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IIAI-AAI.2016.61

Domination dependency analysis of sales marketing based on multi-label classification using label ordering and cycle chain classification. / Soonsiripanichkul, Boonyarit; Murata, Tomohiro.

Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1048-1053 7557768.

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

Soonsiripanichkul, B & Murata, T 2016, Domination dependency analysis of sales marketing based on multi-label classification using label ordering and cycle chain classification. in Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016., 7557768, Institute of Electrical and Electronics Engineers Inc., pp. 1048-1053, 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016, Kumamoto, Japan, 16/7/10. https://doi.org/10.1109/IIAI-AAI.2016.61
Soonsiripanichkul B, Murata T. Domination dependency analysis of sales marketing based on multi-label classification using label ordering and cycle chain classification. In Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1048-1053. 7557768 https://doi.org/10.1109/IIAI-AAI.2016.61
Soonsiripanichkul, Boonyarit ; Murata, Tomohiro. / Domination dependency analysis of sales marketing based on multi-label classification using label ordering and cycle chain classification. Proceedings - 2016 5th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1048-1053
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