A survey of multiple classifier systems as hybrid systems <s> Financial risks <s> This paper presents a soft computing based bank performance prediction system. It is an ensemble system whose constituent models are a multi-layered feed forward neural network trained with backpropagation (MLFF-BP), a probabilistic neural network (PNN) and a radial basis function neural network (RBFN), support vector machine (SVM), classification and regression trees (CART) and a fuzzy rule based classifier. Further, principal component analysis (PCA) based hybrid neural networks, viz. PCA-MLFF-BP, PCA-PNN and PCA-RBF are also included as constituents of the ensemble. Moreover, GRNN and PNN were trained with a genetic algorithm to optimize the smoothing factors. Two ensembles (i) simple majority voting based and (ii) weightage based are implemented. This system predicts the performance of a bank in the coming financial year based on its previous 2-years' financial data. Ten-fold cross-validation is performed in the training sessions and results are validated with an independent production set. It is demonstrated that the ensemble is able to yield lower Type I and Type II errors compared to its constituent models. Further, the ensemble also outperformed an earlier study [P.G. Swicegood, Predicting poor bank profitability: a comparison of neural network, discriminant analysis and professional human judgement, Ph.D. Thesis, Department of Finance, Florida State University, 1998] that used multivariate discriminant analysis (MDA), MLFF-BP and human judgment. <s> BIB001 </s> A survey of multiple classifier systems as hybrid systems <s> Financial risks <s> While extensive research in data mining has been devoted to developing better classification algorithms, relatively little research has been conducted to examine the effects of feature construction, guided by domain knowledge, on classification performance. However, in many application domains, domain knowledge can be used to construct higher-level features to potentially improve performance. For example, past research and regulatory practice in early warning of bank failures has resulted in various explanatory variables, in the form of financial ratios, that are constructed based on bank accounting variables and are believed to be more effective than the original variables in identifying potential problem banks. In this study, we empirically compare the performance of two sets of classifiers for bank failure prediction, one built using raw accounting variables and the other built using constructed financial ratios. Four popular data mining methods are used to learn the classifiers: logistic regression, decision tree, neural network, and k-nearest neighbor. We evaluate the classifiers on the basis of expected misclassification cost under a wide range of possible settings. The results of the study strongly indicate that feature construction, guided by domain knowledge, significantly improves classifier performance and that the degree of improvement varies significantly across the methods. <s> BIB002 </s> A survey of multiple classifier systems as hybrid systems <s> Financial risks <s> A wide range of classification methods have been used for the early detection of financial risks in recent years. How to select an adequate classifier (or set of classifiers) for a given dataset is an important task in financial risk prediction. Previous studies indicate that classifiers' performances in financial risk prediction may vary using different performance measures and under different circumstances. The main goal of this paper is to develop a two-step approach to evaluate classification algorithms for financial risk prediction. It constructs a performance score to measure the performance of classification algorithms and introduces three multiple criteria decision making (MCDM) methods (i.e., TOPSIS, PROMETHEE, and VIKOR) to provide a final ranking of classifiers. An empirical study is designed to assess various classification algorithms over seven real-life credit risk and fraud risk datasets from six countries. The results show that linear logistic, Bayesian Network, and ensemble methods are ranked as the top-three classifiers by TOPSIS, PROMETHEE, and VIKOR. In addition, this work discusses the construction of a knowledge-rich financial risk management process to increase the usefulness of classification results in financial risk detection. <s> BIB003 </s> A survey of multiple classifier systems as hybrid systems <s> Financial risks <s> The ability to accurately predict business failure is a very important issue in financial decision-making. Incorrect decision-making in financial institutions is very likely to cause financial crises and distress. Bankruptcy prediction and credit scoring are two important problems facing financial decision support. As many related studies develop financial distress models by some machine learning techniques, more advanced machine learning techniques, such as classifier ensembles and hybrid classifiers, have not been fully assessed. The aim of this paper is to develop a novel hybrid financial distress model based on combining the clustering technique and classifier ensembles. In addition, single baseline classifiers, hybrid classifiers, and classifier ensembles are developed for comparisons. In particular, two clustering techniques, Self-Organizing Maps (SOMs) and k-means and three classification techniques, logistic regression, multilayer-perceptron (MLP) neural network, and decision trees, are used to develop these four different types of bankruptcy prediction models. As a result, 21 different models are compared in terms of average prediction accuracy and Type I & II errors. By using five related datasets, combining Self-Organizing Maps (SOMs) with MLP classifier ensembles performs the best, which provides higher predication accuracy and lower Type I & II errors. <s> BIB004
Uncertainty in the financial operations is identified with the financial risks such as credit, business, investment, and operational risks. Financial distress can be detected by clustering and MCS in four different combination models. Clustering is performed by classical SOM and k-means algorithms and used to partition the data space prior to MCS training BIB004 . Experimental framework for the evaluation of financial risk assessment models, giving a specific performance measures allow the exploration of computational solutions to these problems BIB003 . Several conventional classifiers and MCS have been tested in this framework using a large pool of datasets. Bank performance and bankruptcy prediction is addressed using a widely heterogenous MCS including PNN,RBF, MLP, SVM, CART trees, and a fuzzy rule system. The effect of PCA initial dimensionality reduction is also tested BIB001 . The effect of feature construction from previous experience and a priori information in the efficiency of classifiers for early warning of bank failures is reported in BIB002 .