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Dimensionality Reduction Using Graph Weighted Subspace Learning for Bankruptcy Prediction

  • Bernardete RibeiroEmail author
  • Ning Chen
Chapter
Part of the Annals of Information Systems book series (AOIS, volume 17)

Abstract

Bankruptcy prediction is an extremely actual and important topic in the world. In this complex problem, dimensionality reduction becomes important easing both tasks of visualization and classification. Despite the different motivations, these algorithms can be cast in a graph embedding framework. In this paper we address weighted graph subspace learning methods for dimensionality reduction of bankruptcy data. The rationale behind re-embedding the data in a lower dimensional space that would be better filled is twofold: to get the most compact representation (visualization) and to make subsequent processing of data more easy (classification). To achieve this, two graph weighted subspace learning models are investigated, namely graph regularized non-negative matrix factorization (GNMF) and spatially smooth subspace learning (SSSL). Through an affinity weight graph matrix, the geometric properties are embedded explicitly into the submanifold lying in the high-dimensional data, consequently, the resulting subspace models allow compact representations able to enhance visualization, clustering and classification. The experimental results on a real world database of French companies show that the graph weighted subspace learning models used in a supervised learning manner are very effective for bankruptcy prediction.

Keywords

Support Vector Machine Credit Risk Generalize Regression Neural Network Rand Index Local Linear Embedding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.CISUC, Department of Informatics EngineeringUniversity of CoimbraCoimbraPortugal
  2. 2.GECADInstituto Superior de Engenharia do PortoPortoPortugal

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