Bankruptcy Analysis for Credit Risk using Manifold Learning



We apply manifold learning to a real data set of distressed and healthy
companies for proper geometric tunning of similarity data points and visualization.
While Isomap algorithm is often used in unsupervised learning our approach
combines this algorithm with information of class labels for bankruptcy prediction.
We compare prediction results with classifiers such as Support Vector
Machines (SVM), Relevance Vector Machines (RVM) and the simple k-Nearest
Neighbor (KNN) in


International Conference on Neural Information Processing, November 2008

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