Prediction of credit delinquents using locally transductive multi-layer perceptron

Many credit data classification problems require label predictions only for a given unlabeled test set. Since the number of an available unlabeled test data set is much larger than a labeled data set, it is desirable to build a predictive model in a transductive setting that takes advantage of the unlabeled data as well as labeled data. This paper proposes a localized transduction based multi-layer perceptron (MLP) methodology to build a better classifier. We provide a practical framework for our methodology. Simulations on real credit delinquents detection problems are conducted to test the proposed method with a promising result.