Authors:
Farid Beninel
1
and
Christophe Biernacki
2
Affiliations:
1
CREST-ENSAI & UMR 6086, France
;
2
Université Lille1, UFR de Mathématiques & UMR 6524, France
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.