Monotone Decision Trees and Noisy Data
AbstractThe 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.
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Bibliographic InfoPaper provided by Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam. in its series Research Paper with number ERS-2002-53-LIS.
Date of creation: 17 Jun 2002
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ordinal classification; monotone decision trees; pruning; noise;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2002-06-24 (All new papers)
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