Monotone Decision Trees and Noisy Data

Cor Bioch and Viara Popova

ERIM Report Series Research in Management from 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

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: monotone decision trees; noise; ordinal classification; pruning (search for similar items in EconPapers)
JEL-codes: C6 M M11 R4 (search for similar items in EconPapers)
Date: 2002-06-17
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Citations: View citations in EconPapers (1)

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