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.
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© 2002 Springer-Verlag Berlin Heidelberg
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Magdon-Ismail, M., Chen, HC.(., Abu-Mostafa, Y.S. (2002). The Multilevel Classification Problem and a Monotonicity Hint. In: Yin, H., Allinson, N., Freeman, R., Keane, J., Hubbard, S. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2002. IDEAL 2002. Lecture Notes in Computer Science, vol 2412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45675-9_61
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DOI: https://doi.org/10.1007/3-540-45675-9_61
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