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Real World Data Mining Applications pp 155–178Cite as

Dimensionality Reduction Using Graph Weighted Subspace Learning for Bankruptcy Prediction

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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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Notes

  1. 1.

    Coface is one of largest financial groups in France providing Credit Insurance, the Factoring Information & Ratings and Debt Recovery.

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Author information

Authors and Affiliations

  1. CISUC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Bernardete Ribeiro

  2. GECAD, Instituto Superior de Engenharia do Porto, Porto, Portugal

    Ning Chen

Authors
  1. Bernardete Ribeiro
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  2. Ning Chen
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Correspondence to Bernardete Ribeiro .

Editor information

Editors and Affiliations

  1. Research & Advanced Engineering, Ford Motor Company, Dearborn, Michigan, USA

    Mahmoud Abou-Nasr

  2. Universität Hamburg Inst. Wirtschaftsinformatik, Hamburg, Germany

    Stefan Lessmann

  3. Universität Hamburg Inst. Wirtschaftsinformatik, Hamburg, Germany

    Robert Stahlbock

  4. Deptartment of Computer & Information Science, Fordham University, Bronx, New York, USA

    Gary M. Weiss

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Ribeiro, B., Chen, N. (2015). Dimensionality Reduction Using Graph Weighted Subspace Learning for Bankruptcy Prediction. In: Abou-Nasr, M., Lessmann, S., Stahlbock, R., Weiss, G. (eds) Real World Data Mining Applications. Annals of Information Systems, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-07812-0_9

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