Generating multi-modality virtual samples with soft DBSCAN for small data set learning

Liang Sian Lin, Der-Chiang Li, Wei Hao Yu, Yu Mei Hsueh

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

Abstract

Owing to the factors of cost and time limit, the number of samples is usually small in the early stages of manufacturing systems. When the number of available data is small, traditional statistic techniques have difficulty to obtain robust analyses. Therefore, based on a uni-modality distribution assumption, many researchers have proposed virtual sample generation methods to expand the training sample size to enhance the performance of small data set learning. In practice, small data may be following a multi-modality distribution. Therefore, in order to solve multi-modal small data sets, this study proposes a new approach to create multi-modality Weibull virtual samples, where we use the maximal p-value to estimate parameters of the Weibull distribution. In addition, the soft DBSCAN method is used to identify a suitable number of modalities. One data set is employed to check the performance of the proposed method, and comparisons are made by the prediction on root mean square error. The results using a paired t-test show that the proposed method has a superior prediction performance than that of the mega-trend-diffusion method using a uni-modality triangular membership function.

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.
Pages363-368
Number of pages6
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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  • Cite this

    Lin, L. S., Li, D-C., Yu, W. H., & Hsueh, Y. M. (2015). Generating multi-modality virtual samples with soft DBSCAN for small data set learning. In K. Tsuchida, N. Ishii, T. Goto, & S. Takahashi (Eds.), Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015 (pp. 363-368). [7336089] (Proceedings - 3rd International Conference on Applied Computing and Information Technology and 2nd International Conference on Computational Science and Intelligence, ACIT-CSI 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACIT-CSI.2015.69