Realization of Knowledge Auto-extraction for Bayesian Expert System Classifier
Qiang Jin · Geography and Geo-Information Science · 2008
Knowledge acquirement has been a long-term bottleneck in the application of expert system classifier in remote sensing.This paper focused on solving the question of expert knowledge auto-extraction and knowledge-base construction for Bayesian expert classifier.Based on the statistics characteristics of sample points,hypothesis between knowledge and reference information and auto-extraction method was set up.The conditional-probability for each class was realized based on statistical analysis of reference samples.In order to validate the accuracy and validity of the method,classification and accuracy assessment on both simulated data and real study-site data had been carried out.Results showed that the conditional-probabilities could be effectively estimated based on the statistical analysis of the reference samples,which means that the knowledge could be extracted automatically for the Bayesian expert system classifier.The landscape classification using expert system classifier with the auto-extracted knowledge showed a higher accuracy than the classifications of maximum likelihood classifier or decision tree learning classifier.This study solved successfully the bottleneck of knowledge acquirement in Bayesian expert system classifier.