Active Learning with Monotonicity Constraints

Abstract

In many applications of data mining it is known beforehand that the response variable should be increasing (or decreasing) in the attributes. We propose two algorithms to exploit such monotonicity constraints for active learning in ordinal classification in two different settings. The basis of our approach is the observation that if the class label of an object is given, then the monotonicity constraints may allow the labels of other objects to be inferred. For instance, from knowing that loan applicant a is rejected, it can be concluded that all applicants that score worse than a on all criteria should be rejected as well. We propose two heuristics to select good query points. These heuristics make a selection based on a point’s potential to determine the labels of other points. The algorithms, each implemented with the proposed heuristics, are evaluated on artificial and real data sets to study their performance. We conclude that exploitation of monotonicity constraints can be very beneficial in active learning.