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Nonparametric Ordinal Classification with Monotonicity Constraints

In many applications of ordinal classification we know that the class label must be increasing (or decreasing) in the attributes. Such relations are called monotone. We discuss two nonparametric approaches to monotone classification: osdl and moca. Our conjecture is that both methods have a tendency to overfit on the training sample, because their basic class probability estimates are often computed on a few observations only. Therefore, we propose to smooth these basic probability estimates by using weighted k nearest neighbour. Through substantial experiments we show how this adjustment improves the classification performance of osdl considerably. The effect on moca on the other hand is less conclusive.