Belief-evidence fusion through successive rule refinement in a hybrid intelligent system
Date of Publication
2002
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Computer Science
Subject Categories
Computer Sciences
College
College of Computer Studies
Department/Unit
Computer Science
Thesis Adviser
Arnulfo Azcarraga
Abstract/Summary
A hybrid intelligent system that is able to sucessively refine knowledge stored in its rulebase is developed. The existing knowledge (referred to as belief rules), which may initially be defined by experts in a particular domain, is stored in the form of rules in the rulebase and is refined by comparing it with new knowledge (referred to as evidence rules) extracted from data sets trained under a neural network. Based on measurement, assessment and interpretation of rule similarity, belief rules existing in the rulebase may be found to be confirmed, contradicted or left unsupported by the new training data. New evidence rules may also be discovered from a training data set. This rule comparison is unique in the sense that rules are viewed and compared in geometric manner. As rules evolve in existence in the rulebase during the belief-evidence fusion process, their bounds, strengths and certainties are also revised. The hybrid intelligent system is tested with different data sets, including hypothetical data sets and actual data sets. the system outperforms conventional backpropagation neural learning systems in terms of accuracy and predictability especially when the data is sparse or arrives in bursts, or when the initial knowledge is incorrect. Ordering effects inherent in incremental systems, however, is difficult to address as neural network learning can be unpredictable. The performance of the system increases if predictive classification of unclassified test data is performed.
Abstract Format
html
Language
English
Format
Accession Number
TG03756
Shelf Location
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
Physical Description
138 leaves ; 28 cm.
Keywords
Hybrid systems; Neural networks (Computer science)
Recommended Citation
Marcos, N. (2002). Belief-evidence fusion through successive rule refinement in a hybrid intelligent system. Retrieved from https://animorepository.dlsu.edu.ph/etd_doctoral/947
