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Authors: Farid Beninel 1 and Christophe Biernacki 2

Affiliations: 1 CREST-ENSAI & UMR 6086, France ; 2 Université Lille1, UFR de Mathématiques & UMR 6524, France

ISBN: 978-989-8111-66-1

Keyword(s): Credit scoring, Discriminant rule, Error rate, Learning sample, Logistic model, Misclassification rate, Generalized discrimination, Updating a discriminant rule, Subpopulations mixture, Supervised classification.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: Often a discriminant rule to predict individuals from a certain subpopulation is given, but the individuals to predict belong to another subpopulation. Two distinct approaches are usually implemented. The first approach is to apply the same discriminant rule for the two subpopulations. The second approach is to estimate a new rule for the second subpopulation. The first classical approach does not take into account differences between subpopulations. The second approach is not reliable in cases of few available individuals from the second subpopulation. In this paper we develop an intermediate approach: we get a rule to predict in the second population combining the experienced rule of the first population and the available learning sample from the second. Different models combining the first rule and the labeled sample from the second population are estimated and tested.

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Paper citation in several formats:
Beninel F.; Biernacki C. and (2009). UPDATING A LOGISTIC DISCRIMINATION RULE - Comparing Some Logistic Submodels in Credit-scoring.In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8111-66-1, pages 267-274. DOI: 10.5220/0001662302670274

@conference{icaart09,
author={Farid Beninel and Christophe Biernacki},
title={UPDATING A LOGISTIC DISCRIMINATION RULE - Comparing Some Logistic Submodels in Credit-scoring},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2009},
pages={267-274},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001662302670274},
isbn={978-989-8111-66-1},
}

TY - CONF

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - UPDATING A LOGISTIC DISCRIMINATION RULE - Comparing Some Logistic Submodels in Credit-scoring
SN - 978-989-8111-66-1
AU - Beninel, F.
AU - Biernacki, C.
PY - 2009
SP - 267
EP - 274
DO - 10.5220/0001662302670274

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