Options
Applying evolutionary algorithms to discover knowledge from medical databases
Date Issued
1999
Publisher
IEEE
ISSN
08843627
Citation
Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1999, vol. 5, pp. V-936 - V-941
Type
Conference Proceedings
Abstract
Data mining has become an important research topic. The increasing use of computer results in an explosion of information. These data can be best used if the knowledge hidden can be uncovered. Thus there is a need for a way to automatically discover knowledge from data. In this paper, new approaches for knowledge discovery from two medical databases are investigated. Two different kinds of knowledge, namely rules and causal structures, are learned. Rules capture interesting patterns and regularities in the databases. Causal structures represented by Bayesian networks capture the causality relationships among the attributes. We employ advanced evolutionary algorithms for these discovery tasks. In particular, Generic Genetic Programming is employed as rule learning algorithm. Our approach for discovering causality relationships is based on Evolutionary Programming which learns Bayesian network structures.
Loading...
Availability at HKSYU Library
