Hybrid computational intelligence models with symbolic rule extraction for pattern classification

Loading...
Thumbnail Image
Date
2008-12
Authors
Ali Quteishat, Anas Mohammad
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
This thesis is concerned with the development of hybrid Computational Intelligence (CI) models for tackling pattern classification problems. The research focuses on hybridization of the Artificial Neural Network (ANN) and Genetic Algorithm (GA) models for data-based learning and rule extraction. In addition, a novel Multi-Agent Classifier System (MACS) is devised to combine the hybrid Cl models into a common framework for pattern classification. The Fuzzy Min-Max (FMM) network is employed as the backbone for developing, the hybrid Cl models in this research. Two novel modifications are proposed to improve the performance of FMM and to extract symbolic if-then rules from its knowledge base. The first modification aims to reduce the number of hyperboxes formed in FMM and to enhance its performance. The second modification incorporates the GA to extract fuzzy if-then rules from FMM-based networks. To further improve the robustness and performance of FMM-based networks for pattern classification, a new MACS, developed based on the Trust-Negotiation- Communication (TNC) reasoning model of multi-agent systems, is introduced. A novel trust measurement method based on the Bayesian decision theory is formulated. The performances of the MACS are evaluated using benchmark as well as real industrial and medical problems, and the bootstrap method is used to quantify the performances statistically. The results ascertain the effectiveness of employing the MACS model for undertaking pattern classification tasks, along with comprehensible if-then rules for explaining their predictions to domain users.
Description
Keywords
Intelligence models with symbolic , Pattern classification
Citation