Monotone Decision Trees and Noisy Data
23 Pages Posted: 18 Feb 2003
Date Written: 17 2002 6,
Abstract
The decision tree algorithm for monotone classification presented in [4, 10] requires strictly monotone data sets. This paper addresses the problem of noise due to violation of the monotonicity constraints and proposes a modification of the algorithm to handle noisy data. It also presents methods for controlling the size of the resulting trees while keeping the monotonicity property whether the data set is monotone or not.
Keywords: ordinal classification, monotone decision trees, noise, pruning
JEL Classification: M, M11, R4, C6
Suggested Citation: Suggested Citation
Bioch, Jan C. and Popova, Viara, Monotone Decision Trees and Noisy Data (17 2002 6,). ERIM Report Series Reference No. ERS-2002-53-LIS, Available at SSRN: https://ssrn.com/abstract=371001
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