Abstract
Credit scoring become an important task to evaluate an applicant by a banker. Many models and tools are available for making initial lending decisions. This paper presents an Hidden Markov Models (HMMs) for modeling credit scoring problems. Baum-Welch algorithm - an iterative process for estimating HMM parameters are often used to developed such models and improve the pattern recognition for many problems. We introduce HMM/Baum-Welch initial model selection: a tool developed to test the impact of choosing initial model to train Baum-Welch process. Experiments results show that the performance of learned models depend on different way of generating the initial models used in credit scoring.
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Benyacoub, B., ElMoudden, I., ElBernoussi, S., Zoglat, A., Ouzineb, M. (2015). Initial Model Selection for the Baum-Welch Algorithm Applied to Credit Scoring. In: Le Thi, H., Pham Dinh, T., Nguyen, N. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. Advances in Intelligent Systems and Computing, vol 360. Springer, Cham. https://doi.org/10.1007/978-3-319-18167-7_31
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DOI: https://doi.org/10.1007/978-3-319-18167-7_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18166-0
Online ISBN: 978-3-319-18167-7
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