Guess-And-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems
This addresses efficiency improvements for expert classification systems, but appears incremental as it builds on existing uncertainty reduction methods.
The paper tackles the problem of reducing the information needed for correct classification in expert systems by leveraging prior probabilities and additional data collection, aiming to minimize average cost or effort.
An expert classification system having statistical information about the prior probabilities of the different classes should be able to use this knowledge to reduce the amount of additional information that it must collect, e.g., through questions, in order to make a correct classification. This paper examines how best to use such prior information and additional information-collection opportunities to reduce uncertainty about the class to which a case belongs, thus minimizing the average cost or effort required to correctly classify new cases.