TY - GEN
T1 - Predictive modeling for default risk using a multilayered feedforward neural network with Bayesian regularization
AU - Duma, Innocent Sizo
AU - Twala, Bhekisipho
AU - Marwala, Tshilidzi
PY - 2013
Y1 - 2013
N2 - In this study we propose a multilayered feedforward neural network (MFNN) with Bayesian Regularization, and apply it to the credit risk evaluation problem domain using a real world data set from a financial services company in England. We choose the MFNN because of its broad applicability to many problem domains of relevance to business: principally prediction, classification, and modelling. We employ two different methods to determine their prowess in identifying the true positives, that is, defaulters. We analyzed the effect of making the number of observed bad equal the number of observed good in the data by over sampling of the minority class (bad obligors) by resampling without replacement, and compare this to the dimensionality reduction of the input vector space using Principal Component Analysis. Overall results indicate that using the Receiver Operating Characteristic as a measure of discriminatory power, over sampling of the minority class has been found to be effective in identifying the true positives.
AB - In this study we propose a multilayered feedforward neural network (MFNN) with Bayesian Regularization, and apply it to the credit risk evaluation problem domain using a real world data set from a financial services company in England. We choose the MFNN because of its broad applicability to many problem domains of relevance to business: principally prediction, classification, and modelling. We employ two different methods to determine their prowess in identifying the true positives, that is, defaulters. We analyzed the effect of making the number of observed bad equal the number of observed good in the data by over sampling of the minority class (bad obligors) by resampling without replacement, and compare this to the dimensionality reduction of the input vector space using Principal Component Analysis. Overall results indicate that using the Receiver Operating Characteristic as a measure of discriminatory power, over sampling of the minority class has been found to be effective in identifying the true positives.
UR - http://www.scopus.com/inward/record.url?scp=84893595050&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2013.6706745
DO - 10.1109/IJCNN.2013.6706745
M3 - Conference contribution
AN - SCOPUS:84893595050
SN - 9781467361293
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2013 International Joint Conference on Neural Networks, IJCNN 2013
T2 - 2013 International Joint Conference on Neural Networks, IJCNN 2013
Y2 - 4 August 2013 through 9 August 2013
ER -