A Generalized Model for Predictive Data Mining

James V. Hansen () and James McDonald
Additional contact information
James V. Hansen: Brigham Young University

Information Systems Frontiers, 2002, vol. 4, issue 2, No 4, 179-186

Abstract: Abstract This paper describes a flexible model for predictive data mining, EGB2, which optimizes over a parameter space to fit data to a family of models based on maximum-likelihood criteria. It is also shown how EGB2 can integrate asymmetric costs of Type I and Type II errors, thereby minimizing expected misclassification costs. Importantly, it has been shown that standard methods of computing maximum-likelihood estimators are generally inconsistent when applied to sample data having different proportions of labels than are found in the universe from which the sample is drawn. We show how a choice estimator based on weighting each observation's contribution to the log-likelihood function, can contribute to estimator consistency and how this feature can be implemented in EGB2.

Keywords: data mining; prediction; choice estimator; misclassification costs (search for similar items in EconPapers)
Date: 2002
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1023/A:1016050803099 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infosf:v:4:y:2002:i:2:d:10.1023_a:1016050803099

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10796

DOI: 10.1023/A:1016050803099

Access Statistics for this article

Information Systems Frontiers is currently edited by Ram Ramesh and Raghav Rao

More articles in Information Systems Frontiers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().