KAMAL PREMARATNE, Ph.D.
Professor
Department of Electric and Computer Engineering
University of Miami
THURSDAY, FEBRUARY 14, 2013
2:00 p.m.– 3:00 pm
Clinical Research Building
6th Floor, Room 692
1120 NW 14th Street
Miami, Florida
RSVP: MICHELE GOMEZ mgomez6@biostat.med.miami.edu
Mining for Rules in Data:
Modeling Imperfect Implication Rules
Rule mining involves the extraction of rules of the type “if A then B” from data. Rules extracted from a finite set of (potentially imperfect) data can hardly be considered perfect. Expert opinions, which are often expressed in terms of natural language statements, tend to generate rules of the type “if A then B with a confidence of 70%”, or more likely, “if A then B with a confidence between 70% and 80%”. In reality, the situation is even more complicated because of the uncertainty or ambiguity regarding the occurrence of the rule antecedent A (e.g., . “I am 75% confident that A occurred”). Such imperfect implication rules are central to any process of reasoning because they capture how humans express knowledge and uncertainty. How can we model imperfect implication rules and use them for extraction of knowledge? The Bayesian approach does not appear to be well equipped to handle imperfect implication rules. In this talk, we provide a brief introduction to Dempster-Shafer (DS) belief theory and show how it can provide an effective framework for modeling imperfect implication rules, and in general imperfect logic constructs.