Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming

  • Authors:

  • Athanasios Tsakonas;George Dounias;Michael Doumpos;Constantin Zopounidis

  • Affiliations:

  • Department of Production and Management Engineering, Demokritus University of Thrace, 12 Vas. Sofias St., Xanthi, Greece;Department of Financial and Management Engineering, Business School, University of the Aegean, 31 Fostini Street, 82100 Chios, Greece;Department of Production Engineering and Management, Technical University of Crete, University Campus, 73100 Chania, Greece;Financial Engineering Laboratory, Department of Production Engineering and Management, Technical University of Crete, University Campus, 73100 Chania, Greece

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2006

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Abstract

The paper demonstrates the efficient use of hybrid intelligent systems for solving the classification problem of bankruptcy. The aim of the study is to obtain classification schemes able to predict business failure. Previous attempts to form efficient classifiers for the same problem using intelligent or statistical techniques are discussed throughout the paper. The application of neural logic networks by means of genetic programming is proposed. This is an advantageous approach enabling the interpretation of the network structure through set of expert rules, which is a desirable feature for field experts. These evolutionary neural logic networks are consisted of an innovative hybrid intelligent methodology, by which evolutionary programming techniques are used for obtaining the best possible topology of a neural logic network. The genetic programming process is guided using a context-free grammar and indirect encoding of the neural logic networks into the genetic programming individuals. Indicative classification results are presented and discussed in detail in terms of both, classification accuracy and solution interpretability.