Monotone Decision Trees and Noisy Data

23 Pages Posted: 18 Feb 2003

See all articles by Jan C. Bioch

Jan C. Bioch

Erasmus University Rotterdam (EUR) - Centre for Computers and Law; Erasmus Research Institute of Management (ERIM)

Viara Popova

Erasmus University Rotterdam (EUR) - Centre for Computers and Law; Erasmus Research Institute of Management (ERIM)

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

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

Jan C. Bioch (Contact Author)

Erasmus University Rotterdam (EUR) - Centre for Computers and Law ( email )

3000 DR Rotterdam
Netherlands

Erasmus Research Institute of Management (ERIM)

P.O. Box 1738
3000 DR Rotterdam
Netherlands

Viara Popova

Erasmus University Rotterdam (EUR) - Centre for Computers and Law ( email )

3000 DR Rotterdam
Netherlands

Erasmus Research Institute of Management (ERIM)

P.O. Box 1738
3000 DR Rotterdam
Netherlands

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