A hybrid architecture for performance reasoning in classification systems
نویسندگان
چکیده
This paper presents a uni ed methodology for reasoning in classi cation systems. The methodology is based on a two-stage structure that incorporates both neural and Bayesian formulations in the rst stage and a rule-based system created by extracting rules from both the classi ers in the second stage. The rule-based system provides a measure of the cause-e ect relationship between the inputs and the outputs. This is a novel and useful method for reasoning about the performance of classi er systems and for representing qualitative knowledge about the causal relationship in decisionmaking systems. The proposed system is tested and results are reported for the problem of automatic target
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