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

A. Martin, V. Aswathy, S. Balaji, T. Lakshmi, V. Prasanna Venkatesan
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引用次数: 9

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.
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基于模糊ID3和蚁群优化算法的定性破产预测分析
有许多定性破产预测模型可用。这些模型使用非财务信息作为定性因素来预测破产。在以往的研究中,采用遗传算法生成定性破产预测规则。然而,该模型只使用了很少数量的定性因素,并且生成的规则存在冗余和重叠。为了提高预测精度,我们提出了一种采用模糊ID3算法对更多定性因子进行分类的模型,并采用蚁群优化算法(ACO)生成预测规则。在模糊ID3中,熵和信息增益的概念有助于对定性参数进行排序,并可用于定性破产预测中生成预测规则。蚁群算法中信息素存储和更新的概念减少了破产预测中的假负规则。蚁群算法的启发式和概率性提高了破产预测的准确性。通过使用这两种算法,我们提供了更准确的预测。
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