Using Boolean Differences for Discovering Ill-Defined Attributes in Propositional Machine Learning

摘要

The accuracy of the rules produced by a concept learning system can be hindered by the presence of errors in the data. Although these errors are most commonly attributed to random noise, there also exist "ill-defined" attributes that are too general or too specific that can produce systematic classification errors. We present a computer program called Newton which uses the fact that ill-defined attributes create an ordered error pattern among the instances to compute hypotheses explaining the classification errors of a concept in terms of too general or too specific attributes. Extensive empirical testing shows that Newton identifies such attributes with a prediction rate over 95%.