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Abstract: Ensemble learning is a method to improve the performance of classification and prediction algorithms. Many studies have demonstrated that ensemble learning can decrease the generalization error and improve the performance of individual classifiers and predictors. However, its performance can be degraded due to multicollinearity problem where multiple classifiers of an ensemble are highly correlated with. This paper proposes a genetic algorithm-based coverage optimization technique in the purpose of resolving multicollinearity problem. Empirical results with bankruptcy prediction on Korea firms indicate that the proposed coverage optimization algorithm can help to design a diverse and highly accurate classification system. [Copyright &y& Elsevier]

Abstract: In this paper, we consider the problem of generating concise but accurate naive Bayes classifiers using taxonomy of propositionalized attributes. For the problem, we introduce propositionalized attribute taxonomy guided naive Bayes Learner (PAT-NBL), a machine learning algorithm that effectively utilizes taxonomy to generate compact classifiers. We extend classical naive Bayes learner to the PAT-NBL algorithm that traverses over a propositionalized taxonomy to search for a locally optimal cut. PAT-NBL uses bottom-up search to find the locally optimal cut on a given taxonomy. For the evaluation of candidate cuts, we apply conditional log-likelihood, conditional minimum description length, and conditional Akaike information criterion. The detected cut enables PAT-NBL to construct an instance space which corresponds to the taxonomy and the data. That is, after PAT-NBL determines a cut according to its information-theoretic criteria, the algorithm generates a concise naive Bayes classifier based on the cut. Our experimental results on UCI Machine Learning benchmark data sets indicate that the proposed algorithm can generate naive Bayes classifiers that are more compact and often comparably accurate to those produced by standard naive Bayes learners. [Copyright &y& Elsevier]

Abstract: 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 problems using mostly decision trees as base classifiers. In this paper, we propose an ensemble with neural network for improving the performance of traditional neural networks on bankruptcy prediction tasks. Experimental results on Korean firms indicated that the bagged and the boosted neural networks showed the improved performance over traditional neural networks. [Copyright &y& Elsevier]