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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(More)
In a bankruptcy prediction model, the accuracy is one of crucial performance measures due to its significant economic impact. Ensemble is one of widely used methods for improving the performance of classification and prediction models. Two popular ensemble methods, Bagging and Boosting, have been applied with great success to various machine learning(More)
In classification or prediction tasks, data imbalance problem is frequently observed when most of instances belong to one majority class. Data imbalance problem has received considerable attention in machine learning community because it is one of the main causes that degrade the performance of clas-sifiers or predictors. In this paper, we propose geometric(More)
Ensemble learning is a method to improve the performance of classification and prediction algorithms. It has received considerable attention because of its prominent generalization and performance improvement. However, its performance can be degraded due to multicollinearity problem where multiple classifiers of an ensemble are highly correlated with. This(More)
In classification or prediction tasks, data imbalance problem is frequently observed when most of samples belong to one majority class. Data imbalance problem has received a lot of attention in machine learning community because it is one of the causes that degrade the performance of classifiers or predictors. In this paper, we propose geometric mean based(More)