Sparse Bayesian Models: Bankruptcy-Predictors of Choice?

Abstract— Making inferences and choosing appropriate responses based on incomplete, uncertainty and noisy data is challenging in financial settings particularly in bankruptcy detection. In an increasingly globalized economy, bankruptcy results both in huge economic losses and tremendous social impact. While early prediction for a bankruptcy, if done appropriately, is of great importance to banks, insurance firms, creditors, and investors, the need of substantially more accurately predicting models becomes crucial. This problem has been approached by various methods ranging from statistics to machine learning, however they find a class decision estimate rather than a probabilistic confidence of the class distribution. In this paper we show that sparse Bayesian models also known as Relevance Vector Machine (RVMs) are superior to the stateof-the-art machine learning algorithms such as Support Vector Machines (SVMs) therefore leading to predictors of choice. The advantage of RVM appr...

• Type: Conference
• Year: 2006
• Where: IJCNN
• Authors: Bernardete Ribeiro, Armando Vieira, João Carvalho das Neves
• Keywords: Decision Function, IJCNN 2006, Machine Learning, Vector Machines