An Analysis on Qualitative Bankruptcy Prediction Rules using Ant-Miner

An Analysis on Qualitative Bankruptcy Prediction Rules using Ant-Miner

Автор: A. Martin, T. Miranda Lakshmi, V. Prasanna Venkatesan

Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa

Статья в выпуске: 1 vol.6, 2013 года.

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Qualitative bankruptcy prediction rules represent experts' problem-solving knowledge to predict qualitative bankruptcy. The objective of this research is predicting qualitative bankruptcy using ant-miner algorithm. Qualitative data are subjective and more difficult to measure. This approach uses qualitative risk factors which include fourteen internal risk factors and sixty eight external risk factors associated with it. By using these factors qualitative prediction rules are generated using ant-miner algorithm and the influence of these factors in bankruptcy is also analyzed. Ant-Miner algorithm is a application of ant colony optimization and data mining concepts. Qualitative rules generated by ant miner algorithm are validated using measure of agreement. These prediction rules yields better accuracy with lesser number of terms than previously applied qualitative bankruptcy prediction methodologies.

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Ant-Miner, Qualitative Bankruptcy Prediction, Experts Decision Analysis, Data Mining, Kappa Test, Measure of Agreement, Bankruptcy

Короткий адрес: https://sciup.org/15010513

IDR: 15010513

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