Combinação de classificadores para inferência dos rejeitados

Abstract

In credit scoring problems, the interest is to associate to an element who request some kind of credit, a probability of default. However, traditional models uses samples biased because the data obtained from the tenderers has only clients who won a approval of a request for previous credit. In order to reduce the bias sample of these models, we use strategies to extract information about individuals rejected to be able to infer a response, good or bad payer. This is what we call the reject inference. With the use of these strategies, we also use the bagging technique (bootstrap aggregating), which consist in generate models based in some bootstrap samples of the training data in order to get a new predictor, when these models is combined. In this work we will discuss about some of the combination methods in the literature, especially the method of combination by logistic regression, although little used but with interesting results.We'll also discuss some strategies relating to reject inference. Analyses are given through a simulation study, in data sets generated and real data sets of public domain.