The Multilevel Classification Problem and a Monotonicity Hint

Authors:
Malik Magdon-Ismail;Hung-Ching Chen;Yaser S. Abu-Mostafa
Affiliations:
-;-;-
Venue:
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Year:
2002

Citing 2
Cited 2

Hints

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Abstract

We introduce and formalize the multilevel classification problem, in which each category can be subdivided into different levels. We analyze the framework in a Bayesian setting using Normal class conditional densities. Within this framework, a natural monotonicity hint converts the problem into a nonlinear programming task, with non-linear constraints. We present Monte Carlo and gradient based techniques for addressing this task, and show the results of simulations. Incorporation of monotonicity yields a systematic improvement in performance.