Inductive classifier learning from data: An extended Bayesian belief function approach

A central problem in artificial intelligence is reasoning under uncertainty. This thesis views inductive learning as reasoning under uncertainty and develops an Extended Bayesian Belief Function approach that allows a two-layer representation of the probabilistic rules: basic probabilistic belief and their confidences, which are independent of each other and represent different semantics of the rules. The use of the confidence measure of probabilistic rules can thus handle many difficult problems in inductive learning, including noise, missing values, small samples, inter-attribute dependency, and irrelevant or partially relevant attributes, all of which are characteristics of real-world induction tasks.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Artificial Intelligence

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Ma, Yong

Contributors dc:contributor

• Wilkins, David C.

• Artificial Intelligence
• Computer Science

Statement dc:rights

• Copyright 1995 Ma, Yong

Language dc:language

eng

Identifier

AAI9624423
(UMI)AAI9624423

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/23508