Improving knowledge acquisition capability of M5' model tree on small datasets

Chun Hao Tsai, Der Chiang Li

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

2 Citations (Scopus)

Abstract

In many small dataset learning tasks, however, owing to the incomplete data structure, the explicit information for decision making is limited. This research aims to learn more information hidden inside the incomplete data by adding more samples to strengthen data structures. Based on the prior knowledge provided by the M5' model tree, the proposed research mechanism generates artificial samples to enhance the crisp data structures. Besides, the ability to handle nominal attributes is also provided in this research, while it is usually lacking in most sample generation approaches. In the experiments, the results show that the knowledge acquisition capability and the predictive accuracies of M5' are improved.

Original languageEnglish
Title of host publicationProceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015
EditorsKensei Tsuchida, Naohiro Ishii, Takaaki Goto, Satoshi Takahashi
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages379-386
Number of pages8
ISBN (Electronic)9781467396424
DOIs
Publication statusPublished - 2015 Nov 23
Event3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015 - Okayama, Japan
Duration: 2015 Jul 122015 Jul 16

Publication series

NameProceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015

Other

Other3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015
CountryJapan
CityOkayama
Period15-07-1215-07-16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems

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