Learning manifolds for bankruptcy analysis

Authors:
Bernardete Ribeiro;Armando Vieira;João Duarte;Catarina Silva;João Carvalho Das Neves;Qingzhong Liu;Andrew H. Sung
Affiliations:
CISUC, Department of Informatics Engineering, University of Coimbra, Portugal;Physics Department, Polytechnic Institute of Porto, Portugal;Physics Department, Polytechnic Institute of Porto, Portugal;CISUC, Department of Informatics Engineering, University of Coimbra, Portugal;ISEG, School of Economics, Technical University of Lisbon, Portugal;Computer Science Department, University of New Mexico Tech;Computer Science Department, University of New Mexico Tech
Venue:
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Year:
2008

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Abstract

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 the same data set and we show comparable accuracy of the proposed approach.