A data driven rule-base inference approach for classification systems

Shuwei Chen, Jun Liu, Hui Wang, Juan Carlos Augusto

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

2 Citations (Scopus)

Abstract

This paper proposes a generic data driven inference methodology for rule-based classification systems. The generic rule base is in a belief rule base structure, where the consequent of a rule takes the belief distribution form. Other knowledge representation parameters such as the weights of both input attributes and rules are also considered in this framework. In an established rule base, the matching degree of an input between the antecedents of a rule is firstly computed to get the activation weight for the rule. Then a weighted aggregation of the consequents of activated rules is used for the inference process. Two numerical examples are provided to illustrate the proposed method.
Original languageEnglish
Title of host publication2011 IEEE 2nd International Conference on Software Engineering and Service Science:
Place of PublicationUnited States
Publisher IEEE
Pages78-81
Number of pages4
ISBN (Electronic)978-1-4244-9699-0
ISBN (Print)978-1-4244-9698-3
DOIs
Publication statusPublished - 12 Aug 2011
Externally publishedYes

Publication series

NameIEEE International Conference on Software Engineering and Service Science
PublisherIEEE
ISSN (Print)2327-0586
ISSN (Electronic)2327-0594

Bibliographical note

The 2nd IEEE International Conference on Software Engineering and Service Sciences (ICSESS 2011) ; Conference date: 01-07-2011

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