Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programmingBankruptcy prediction with neural logic networks by means of grammar-guided genetic programming Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2015-11-082006enThe 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.http://creativecommons.org/licenses/by/4.0/Expert Systems with Applications303449-461 Tsakonas Athanasios Dounias, G Michael Doumpos Δουμπος Μιχαλης Zopounidis Konstantinos Ζοπουνιδης Κωνσταντινος Elsevier Bankruptcy Neural logic networks Grammar-guided genetic programming Cellular encoding