The implication of corporate bankruptcy prediction is important to financial institutions when making lending decisions. In related studies, many bankruptcy prediction models have been developed based on some machine-learning techniques. This paper presents a meta-learning framework, which is composed of two-level classifiers for bankruptcy prediction. The first-level multiple classifiers perform the data reduction task by filtering out unrepresentative training data. Then, the outputs of the first-level classifiers are utilized to create the second-level single (meta) classifier. The experiments are based on five related datasets and the results show that the proposed meta-learning framework provides higher prediction accuracy rates and lower type I/II errors when compared with the stacked generalization classifier and other three widely developed baselines, such as neural networks, decision trees, and logistic regression.
Purpose -Customer lifetime value (CLV) has received increasing attention in database marketing. Enterprises can retain valuable customers by the correct prediction of valuable customers. In the literature, many data mining and machine learning techniques have been applied to develop CLV models. Specifically, hybrid techniques have shown their superiorities over single techniques. However, it is unknown which hybrid model can perform the best in customer value prediction. Therefore, the purpose of this paper is to compares two types of commonly-used hybrid models by classification þ classification and clustering þ classification hybrid approaches, respectively, in terms of customer value prediction. Design/methodology/approach -To construct a hybrid model, multiple techniques are usually combined in a two-stage manner, in which the first stage is based on either clustering or classification techniques, which can be used to pre-process the data. Then, the output of the first stage (i.e. the processed data) is used to construct the second stage classifier as the prediction model. Specifically, decision trees, logistic regression, and neural networks are used as the classification techniques and k-means and self-organizing maps for the clustering techniques to construct six different hybrid models. Findings -The experimental results over a real case dataset show that the classification þ classification hybrid approach performs the best. In particular, combining two-stage of decision trees provides the highest rate of accuracy (99.73 percent) and lowest rate of Type I/II errors (0.22 percent/ 0.43 percent). Originality/value -The contribution of this paper is to demonstrate that hybrid machine learning techniques perform better than single ones. In addition, this paper allows us to find out which hybrid technique performs best in terms of CLV prediction.
Issuing a going-concern opinion is a difficult and complex task for auditors.The auditors have to take into account different critical factors in order to make the right decision based on information obtained from the auditing process. This study adopts the so-called "random forest" approach (based on the ensemble method) to assist auditors in making such a decision. To investigate the corresponding effect of the proposed approach, we conduct a series of experiments and a performance comparison. The results show that the random forest method outperforms the baseline methods in terms of the accuracy rate, ROC area, kappa value, type II error, precision, and recall rate. The proposed approach is proven to be more accurate and stable than previous methods.
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