Integrating Economic Knowledge in Data Mining Algorithms
AbstractThe assessment of knowledge derived from databases depends on many factors. Decision makers often need to convince others about the correctness and effectiveness of knowledge induced from data.The current data mining techniques do not contribute much to this process of persuasion.Part of this limitation can be removed by integrating knowledge from experts in the field, encoded in some accessible way, with knowledge derived form patterns stored in the database.In this paper we will in particular discuss methods for implementing monotonicity constraints in economic decision problems.This prior knowledge is combined with data mining algorithms based on decision trees and neural networks.The method is illustrated in a hedonic price model.
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Bibliographic InfoPaper provided by Tilburg University, Center for Economic Research in its series Discussion Paper with number 2001-63.
Date of creation: 2001
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Web page: http://center.uvt.nl
knowledge; neural network; data mining; decision trees;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2001-10-16 (All new papers)
- NEP-CMP-2001-10-16 (Computational Economics)
- NEP-ECM-2001-10-16 (Econometrics)
- NEP-MIC-2001-10-16 (Microeconomics)
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- Misha Beek & Hennie Daniels, 2014. "A Non-parametric Test for Partial Monotonicity in Multiple Regression," Computational Economics, Society for Computational Economics, vol. 44(1), pages 87-100, June.
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