Guess-And-Verify Heuristics for Reducing Uncertainties in Expert Classification Systems

TL;DR

This paper explores how expert classification systems can leverage prior statistical knowledge and additional information to efficiently reduce uncertainty and minimize classification effort.

Contribution

It introduces heuristics for effectively utilizing prior probabilities and information-collection strategies to improve classification accuracy and efficiency.

Findings

Heuristics effectively reduce classification uncertainty.

Prior knowledge decreases the need for additional questions.

Method minimizes average classification cost.

Abstract

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.