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The Effects of Domain Knowledge on the Performance of Data Mining Models for Bank Failures. Wei Ge Atish P. Sinha Huimin Zhao School of Business Administration University of Wisconsin-Milwaukee. Research Objective.
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The Effects of Domain Knowledge on the Performance of Data Mining Models for Bank Failures Wei Ge Atish P. Sinha Huimin Zhao School of Business Administration University of Wisconsin-Milwaukee
Research Objective • To examine the effects of domain knowledge on the performance of data mining models.
Problem Domain • Bankruptcy: early warnings of bank failures. • Data sets • Banks failed in 1991 and 1992. • Each failed bank matched with a survived bank. • Balanced sample (half failed, half survived). • One-year prior (480 cases) and two-year prior prediction (468 cases)
Domain Knowledge • CAMEL financial ratios derived from raw accounting variables • Capital adequacy • Asset quality • Management quality • Earnings • Liquidity • Over 100 ratios developed in past research. • 26 simple ratios adopted in this study.
Methodology • Comparing performance of classifiers learned with & without domain knowledge • 93 original accounting variables • 26 financial ratios • 4 classification techniques: logistic regression, C4.5 decision tree, neural network, k-nearest neighbor • Performance measure: expected misclassification cost
Results • Under various settings of prior probability of failure (0.01, 0.02) and cost ratios (10-100) • Lower cost is observed with domain knowledge for every classification method • Effect size of domain knowledge depends on the method • More improvement on logistic regression and neural network than on other methods
Examples (a)One-year-prior, p=0.02 and r=50 (b) One-year-prior, p=0.02 and r=60
Conclusion • Incorporating domain knowledge improves classifier performance. • Effect of domain knowledge varies depending on classification method used.