A Hybrid Belief Rule-Based Classification System Based on Uncertain Training Data and Expert Knowledge

Lianmeng Jiao, Thierry Denoeux, Quan Pan

Research output: Contribution to journalArticlepeer-review

61 Scopus citations

Abstract

In some real-world classification applications, such as target recognition, both training data collected by sensors and expert knowledge may be available. These two types of information are usually independent and complementary, and both are useful for classification. In this paper, a hybrid belief rule-based classification system (HBRBCS) is developed to make joint use of these two types of information. The belief rule structure, which is capable of capturing fuzzy, imprecise, and incomplete causal relationships, is used as the common representation model. With the belief rule structure, a data-driven belief rule base (DBRB) and a knowledge-driven belief rule base (KBRB) are learned from uncertain training data and expert knowledge, respectively. A fusion algorithm is proposed to combine the DBRB and KBRB to obtain an optimal hybrid belief rule base (HBRB). A belief reasoning and decision-making module is then developed to classify a query pattern based on the generated HBRB. An airborne target classification problem in the air surveillance system is studied to demonstrate the performance of the proposed HBRBCS for combining both uncertain sensor measurements and expert knowledge to make classification.

Original languageEnglish
Article number7365464
Pages (from-to)1711-1723
Number of pages13
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume46
Issue number12
DOIs
StatePublished - Dec 2016

Keywords

  • Belief rule
  • expert knowledge
  • hybrid classification system
  • Uncertain data

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