On the random generation of monotone data sets

Authors:
K. De Loof;B. De Baets;H. De Meyer
Affiliations:
Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 S9, B-9000 Gent, Belgium;Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure links 653, B-9000 Gent, Belgium;Department of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 S9, B-9000 Gent, Belgium
Venue:
Information Processing Letters
Year:
2008

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Abstract

Many of the state-of-the-art classification algorithms for data with linearly ordered attribute domains and a linearly ordered label set insist on the monotonicity of the induced classification rule. Training and evaluation of such algorithms requires the availability of sufficiently general monotone data sets. In this short contribution we introduce an algorithm that allows for the (almost) uniform random generation of monotone data sets based on the Markov Chain Monte Carlo method.