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A hybrid architecture for performance reasoning in classification systems

This paper presents a unified methodology for reasoning in classification systems. The methodology is based on a two-stage structure that incorporates both neural and Bayesian formulations in the first stage and a rule-based system created by extracting rules from both the classifiers in the second stage. The rule-based system provides a measure of the cause-effect relationship between the inputs and the outputs. This is a novel and useful method for reasoning about the performance of classifier systems and for representing qualitative knowledge about the causal relationship in decision-making systems. The proposed system is tested and results are reported for the problem of automatic target detection.