Self-Learning for a. Bayesian Knowledge Base: How Long Does It Take for the Machine to Educate Itself?

Abstract

SummaryA model system has been described which generates “cases” of vaginal discharge. The system was used to define a knowledge base for the application of Bayes’ theorem to the diagnosis of individual cases; this simulation is equivalent to self-learning in an expert system. By using different numbers of cases to define the knowledge base, it was possible to estimate the minimum size of knowledge base required for consistent and accurate diagnosis. The minimum size was found to vary according to the aspect of diagnosis examined. Thus, for overall correct diagnoses at least 200 cases were required, while failure to identify “non-specific” cases was eliminated with only 20 cases in the knowledge base. A simulation of this type is of potential practical value in determining the number of cases required in the knowledge base for a Bayesian system.