Derivation of Monotone Decision Models from Non-Monotone Data

H.A.M. Daniëls, M.V. Velikova

Research output: Working paperDiscussion paperOther research output

361 Downloads (Pure)

Abstract

The objective of data mining is the extraction of knowledge from databases. In practice, one often encounters difficulties with models that are constructed purely by search, without incorporation of knowledge about the domain of application.In economic decision making such as credit loan approval or risk analysis, one often requires models that are monotone with respect to the decision variables involved.If the model is obtained by a blind search through the data, it does mostly not have this property even if the underlying database is monotone.In this paper, we present methods to enforce monotonicity of decision models.We propose measures to express the degree of monotonicity of the data and an algorithm to make data sets monotone.In addition, it is shown that monotone decision trees derived from cleaned data perform better compared to trees derived from raw data.
Original languageEnglish
Place of PublicationTilburg
PublisherOperations research
Number of pages16
Volume2003-30
Publication statusPublished - 2003

Publication series

NameCentER Discussion Paper
Volume2003-30

Keywords

  • decision models
  • knowledge
  • decision theory
  • operational research
  • data mining

Fingerprint Dive into the research topics of 'Derivation of Monotone Decision Models from Non-Monotone Data'. Together they form a unique fingerprint.

  • Cite this

    Daniëls, H. A. M., & Velikova, M. V. (2003). Derivation of Monotone Decision Models from Non-Monotone Data. (CentER Discussion Paper; Vol. 2003-30). Operations research.