Monotone Decision Trees and Noisy Data

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.

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Year of publication:	2002-06-17
Authors:	Bioch, J.C. ; Popova, V.
Institutions:	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.
Subject:	ordinal classification \| monotone decision trees \| noise \| pruning

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Extent:	application/pdf
Series:	Research Paper. - ISSN 1566-5283.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series RePEc:dgr:eureri Number ERS-2002-53-LIS
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005288816