PAKDD Data Mining Competition 2009: New Ways of Using Known Methods

  • Chaim Linhart
  • Guy Harari
  • Sharon Abramovich
  • Altina Buchris
Conference paper

DOI: 10.1007/978-3-642-14640-4_7

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5669)
Cite this paper as:
Linhart C., Harari G., Abramovich S., Buchris A. (2010) PAKDD Data Mining Competition 2009: New Ways of Using Known Methods. In: Theeramunkong T. et al. (eds) New Frontiers in Applied Data Mining. PAKDD 2009. Lecture Notes in Computer Science, vol 5669. Springer, Berlin, Heidelberg

Abstract

The PAKDD 2009 competition focuses on the problem of credit risk assessment. As required, we had to confront the problem of the robustness of the credit-scoring model against performance degradation caused by gradual market changes along a few years of business operation. We utilized the following standard models: logistic regression, KNN, SVM, GBM and decision tree. The novelty of our approach is two-fold: the integration of existing models, namely feeding the results of KNN as an input variable to the logistic regression, and re-coding categorical variables as numerical values that represent each category’s statistical impact on the target label. The best solution we obtained reached 3rd place in the competition, with an AUC score of 0.655.

Keywords

data mining logistic regression KNN credit risk assessment 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chaim Linhart
    • 1
  • Guy Harari
    • 1
  • Sharon Abramovich
    • 2
  • Altina Buchris
    • 2
  1. 1.School of Computer ScienceTel Aviv UniversityTel AvivIsrael
  2. 2.Department of Statistics and Operations ResearchTel Aviv UniversityTel AvivIsrael

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