Predicting Business Failure Using an RSF‐based Case‐Based Reasoning Ensemble Forecasting Method

Case‐based reasoning (CBR) is considered a vital methodology in the current business forecasting area because of its simplicity, competitive performance with modern methods, and ease of pattern maintenance. Business failure prediction (BFP) is an effective tool that helps business people and entrepreneurs make more precise decisions in the current crisis. Using CBR as a basis for BFP can improve the tool's utility because CBR has the potential advantage in making predictions as well as suggestions compared with other methods. Recent studies indicate that an ensemble of various techniques has the possibility of improving the performance of predictive model. This research focuses on an early investigation on predicting business failure using a CBR ensemble (CBRE) forecasting method constructed from the use of random similarity functions (RSF), dubbed RSF‐based CBRE. Four issues are discussed: (i) the reasons for the use of RSF as the basis in the CBRE forecasting method for BFP; (ii) the means to construct the RSF‐based CBRE forecasting method for BFP; (iii) the empirical test on sensitivity of the RSF‐based CBRE to the number of member CBR predictors; and (iv) performance assessment of the ensemble forecasting method. Results of the RSF‐based CBRE forecasting method were statistically validated by comparing them with those of multivariate discriminant analysis, logistic regression, single CBR, and a linear support vector machine. The results from Chinese hotel BFP indicate that the RSF‐based CBRE forecasting method could significantly improve CBR's upper limit of predictive capability. Copyright © 2011 John Wiley & Sons, Ltd.     

Hybridizing kernel‐based fuzzy c‐means with hierarchical selective neural network ensemble model for business failure prediction

More and more ensemble models are used to forecast business failure. It is generally known that the performance of an ensemble relies heavily on the diversity between each base classifier. To achieve diversity, this study uses kernel‐based fuzzy c‐means (KFCM) to organize firm samples and designs a hierarchical selective ensemble model for business failure prediction (BFP). First, three KFCM methods—Gaussian KFCM (GFCM), polynomial KFCM (PFCM), and Hyper‐tangent KFCM (HFCM)—are employed to partition the financial data set into three data sets. A neural network (NN) is then adopted as a basis classifier for BFP, and three sets, which are derived from three KFCM methods, are used to build three classifier pools. Next, classifiers are fused by the two‐layer hierarchical selective ensemble method. In the first layer, classifiers are ranked based on their prediction accuracy. The stepwise forward selection method is employed to selectively integrate classifiers according to their accuracy. In the second layer, three selective ensembles in the first layer are integrated again to acquire the final verdict. This study employs financial data from Chinese listed companies to conduct empirical research, and makes a comparative analysis with other ensemble models and all its component models. It is the conclusion that the two‐layer hierarchical selective ensemble is good at forecasting business failure.