The discovery of experts' decision rules from qualitative bankruptcy data using genetic algorithms

Abstract

Abstract Numerous studies on bankruptcy prediction have widely applied data mining techniques to finding out the useful knowledge automatically from financial databases, while few studies have proposed qualitative data mining approaches capable of eliciting and representing experts' problem-solving knowledge from experts' qualitative decisions. In an actual risk assessment process, the discovery of bankruptcy prediction knowledge from experts is still regarded as an important task because experts' predictions depend on their subjectivity. This paper proposes a genetic algorithm-based data mining method for discovering bankruptcy decision rules from experts' qualitative decisions. The results of the experiment show that the genetic algorithm generates the rules which have the higher accuracy and larger coverage than inductive learning methods and neural networks. They also indicate that considerable agreement is achieved between the GA method and experts' problem-solving knowledge. This means that the proposed method is a suitable tool for eliciting and representing experts' decision rules and thus it provides effective decision supports for solving bankruptcy prediction problems.