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Chapter

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Title

Machine ranking of 2-Uncertain rules acquired from real data

Authors

[ 1 ] Instytut Automatyki i Inżynierii Informatycznej, Wydział Elektryczny, Politechnika Poznańska | [ 2 ] Instytut Matematyki, Wydział Elektryczny, Politechnika Poznańska | [ P ] employee

Year of publication

2013

Chapter type

chapter in monograph

Publication language

english

Keywords
EN
  • attributive data
  • RBS (Rule-Based System)
  • uncertainty
  • reliability
Abstract

EN There are many places (e.g. hospital emergency rooms) where reliable diagnostic systems might support people in their work. They could have form of RBSs with uncertainty and use the techniques of forward and backward chaining in their reasoning. The number and the contents of derived hypotheses depend then both on the form of the system’s knowledge base and on the inference engine performance. The paper provides detailed considerations on designing and applying particular uncertain rules, namely 2-uncertain rules. They are equipped with two reliability factors, representing a kind of second order probability. The rules can be acquired from real data of attributive representation. In the paper we propose a method for calculating the two reliability factors. We also suggest how to take advantage of the factors during reasoning, in order to obtain reliable hypotheses. The factors help to rank the rules and to fire them in the best order.

Pages (from - to)

198 - 222

DOI

10.1007/978-3-642-41776-4_9

URL

https://link.springer.com/chapter/10.1007/978-3-642-41776-4_9

Book

Transactions on Computational Collective Intelligence XI

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