An analysis on Qualitative Bankruptcy Prediction using Fuzzy ID3 and Ant Colony Optimization Algorithm

Abstract

Many Qualitative Bankruptcy Prediction models are available. These models use non-financial information as Qualitative factors to predict Bankruptcy. In the prior researches Genetic Algorithm was applied to generate Qualitative Bankruptcy Prediction Rules. However this Model uses only very less number of Qualitative factors and the generated rules has redundancy and overlapping. To improve the Prediction accuracy we have proposed a model which applies more number of Qualitative factors which can be categorized using Fuzzy ID3 Algorithm and Prediction Rules are generated using Ant Colony Optimization Algorithm (ACO). In Fuzzy ID3 the concept of Entropy and Information Gain helps to rank the qualitative parameters and this can be used to generate prediction rules in qualitative Bankruptcy prediction. The concept of pheromone depositing and updating in Ant Colony Algorithm reduce the false negative rules in the bankruptcy prediction. The heuristic and probabilistic features of Ant Colony Algorithm increase the prediction accuracy of Bankruptcy. By using these two algorithms we provide more accurate prediction.