A survey of multiple classifier systems as hybrid systems <s> Credit risk <s> Credit risk prediction models seek to predict quality factors such as whether an individual will default (bad applicant) on a loan or not (good applicant). This can be treated as a kind of machine learning (ML) problem. Recently, the use of ML algorithms has proven to be of great practical value in solving a variety of risk problems including credit risk prediction. One of the most active areas of recent research in ML has been the use of ensemble (combining) classifiers. Research indicates that ensemble individual classifiers lead to a significant improvement in classification performance by having them vote for the most popular class. This paper explores the predicted behaviour of five classifiers for different types of noise in terms of credit risk prediction accuracy, and how such accuracy could be improved by using classifier ensembles. Benchmarking results on four credit datasets and comparison with the performance of each individual classifier on predictive accuracy at various attribute noise levels are presented. The experimental evaluation shows that the ensemble of classifiers technique has the potential to improve prediction accuracy. <s> BIB001 </s> A survey of multiple classifier systems as hybrid systems <s> Credit risk <s> This paper presents novel neural network-genetic programming hybrids to predict the failure of dotcom companies. These hybrids comprise multilayer feed forward neural network (MLFF), probabilistic neural network (PNN), rough sets (RS) and genetic programming (GP) in a two-phase architecture. In each hybrid, one technique is used to perform feature selection in the first phase and another one is used as a classifier in the second phase. Further t-statistic and f-statistic are also used separately for feature selection in the first phase. In each of these cases, top 10 features are selected and fed to the classifier. Also, the NN-GP hybrids are compared with MLFF, PNN and GP in their stand-alone mode without feature selection. The dataset analyzed here is collected from Wharton Research Data Services (WRDS). It consists of 240 dotcom companies of which 120 are failed and 120 are healthy. Ten-fold cross-validation is performed throughout the study. Results in terms of average accuracy, average sensitivity, average specificity and area under the receiver operating characteristic curve (AUC) indicate that the GP outperformed all the techniques with or without feature selection. The superiority of GP-GP is demonstrated by t-test at 10% level of significance. Furthermore, the results are much better than those reported in previous studies on the same dataset. <s> BIB002 </s> A survey of multiple classifier systems as hybrid systems <s> Credit risk <s> Multiple classifier systems combine several individual classifiers to deliver a final classification decision. In this paper the performance of several multiple classifier systems are evaluated in terms of their ability to correctly classify consumers as good or bad credit risks. Empirical results suggest that some multiple classifier systems deliver significantly better performance than the single best classifier, but many do not. Overall, bagging and boosting outperform other multi-classifier systems, and a new boosting algorithm, Error Trimmed Boosting, outperforms bagging and AdaBoost by a significant margin. <s> BIB003 </s> A survey of multiple classifier systems as hybrid systems <s> Credit risk <s> Recently, high profile cases of financial statement fraud have been dominating the news. This paper uses data mining techniques such as Multilayer Feed Forward Neural Network (MLFF), Support Vector Machines (SVM), Genetic Programming (GP), Group Method of Data Handling (GMDH), Logistic Regression (LR), and Probabilistic Neural Network (PNN) to identify companies that resort to financial statement fraud. Each of these techniques is tested on a dataset involving 202 Chinese companies and compared with and without feature selection. PNN outperformed all the techniques without feature selection, and GP and PNN outperformed others with feature selection and with marginally equal accuracies. <s> BIB004 </s> A survey of multiple classifier systems as hybrid systems <s> Credit risk <s> Enterprise credit risk assessment has long been regarded as a critical topic and many statistical and intelligent methods have been explored for this issue. However there are no consistent conclusions on which methods are better. Recent researches suggest combining multiple classifiers, i.e., ensemble learning, may have a better performance. In this paper, we propose a new hybrid ensemble approach, called RSB-SVM, which is based on two popular ensemble strategies, i.e., bagging and random subspace and uses Support Vector Machine (SVM) as base learner. As there are two different factors, i.e., bootstrap selection of instances and random selection of features, encouraging diversity in RSB-SVM, it would be advantageous to get better performance. The enterprise credit risk dataset, which includes 239 companies' financial records and is collected by the Industrial and Commercial Bank of China, is selected to demonstrate the effectiveness and feasibility of proposed method. Experimental results reveal that RSB-SVM can be used as an alternative method for enterprise credit risk assessment. <s> BIB005 </s> A survey of multiple classifier systems as hybrid systems <s> Credit risk <s> 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. <s> BIB006
Credit risk prediction models seek to predict whether an individual will default on a loan or not. It is greatly affected by the unavailability, scarcity and incompleteness of data. The application of machine learning to this problem includes the evaluation of bagging, boosting, stacking as well as other conventional classifiers over three benchmarking datasets, including sensitivity to noise added to the attributes BIB001 . Another approach for this problem is the Error Trimmed Boosting (ETB) BIB003 which has been tested over a privative dataset provided by a company. ETB consists in the iterative selection of subsets of samples based on their error under the current classifier. An special case of credit risk is enterprise risk assessment which has a strong economic effect due to the financial magnitude of the entities involved. To deal with this problem a combination of bagging and random subspace feature selection using SVM as the base classifier has been developed and tested. The resulting method has increased diversity improving results over a dataset provided by the Bank of China BIB005 . Bankruptcy prediction is a dramatic special case of credit risk. Ensemble systems with diversity ensured by genetic algorithm based selection of component classifiers is proposed in BIB006 for bankruptcy prediction in South Korean firms. The prediction of failure of dotcom companies has been a matter of research since the bubble explosion after the year 2000. Tuning a hybrid of PNN, MLP and genetic programming classifiers over a set of features selected applying a t-test and F-test for relevance to the categorical variable has given some solutions BIB002 . The same approach is reported in BIB004 to detect fraud in the financial statement of big companies.