The refinement of probabilistic rule sets: Sociopathic interactions

Abstract

Probabilistic rules in a classification expert system can result in a sociopathic knowledge base, as a consequence of the assumption of conditional independence between observations and rule modularity. A sociopathic knowledge base has the property that all the rules are individually judged to be correct rules, yet a subset of the knowledge base gives better classification accuracy than the original knowledge base, independent of the amount of computational resources that are available. This paper describes how sociopathic interactions cause rule induction and refinement methods to converge to local optima with respect to maximizing classification accuracy. The problem of optimally refining sociopathic knowledge bases is modeled as a bipartite graph minimization problem and shown to be NP-hard. A heuristic rule refinement algorithm for sociopathic reduction, called SOCIO-REDUCER, is presented. Experimental results in a medical diagnosis domain show that it can reduce the diagnosis error rate by 10.5%.