Self-Adapted and Filtered Qualitative Maximum a Posterior Algorithm for Small Data Sets

Hui Cao, Xiaoguang Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper studies Bayesian network parameter learning from small data sets. Among existing techniques for parameter estimation in Bayesian network, qualitative maximum a posterior (QMAP) is state-of-the-art for small data sets. However, the uncertainty of pseudo prior statistic counts and the interference information in the broad prior constraints given by domain experts limit its performance. To further improve the learning accuracy and to eliminate interference information from given constraints, a self-adapted and filtered QMAP (SFQMAP) algorithm is proposed in this paper. The algorithm improves the learning performance by exploiting proper quantities of pseudo prior statistic count and applying the filtration model in QMAP. Via experiments on a series of examples we demonstrate that our approach is significantly more accurate than existing techniques for parameter learning from small data sets.

Original languageEnglish
Title of host publicationProceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1585-1591
Number of pages7
ISBN (Electronic)9781538666142
DOIs
StatePublished - 22 Jan 2019
Event20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018 - Exeter, United Kingdom
Duration: 28 Jun 201830 Jun 2018

Publication series

NameProceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018

Conference

Conference20th International Conference on High Performance Computing and Communications, 16th IEEE International Conference on Smart City and 4th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018
Country/TerritoryUnited Kingdom
CityExeter
Period28/06/1830/06/18

Keywords

  • Bayesian network
  • Parameter learning
  • Qualitative maximum a posterior
  • Self-adapted and filtered
  • Small data sets

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