Application of hybrid case-based reasoning for enhanced performance in bankruptcy prediction

Explanatory Factors of Business Failure: Literature Review and Global Trends

This study aims to provide a bibliometric analysis of business failure research, recognise the main existing research topics and establish future research challenges. The results, based on a sample of 588 articles, show that the number of published papers and citations has grown steadily, especially in the last 14 years. The most productive and relevant journals, countries, institutions and authors are presented using bibliometric performance indicators. In addition, through the graphical mapping of strategic diagrams, this study identifies the most significant research trends and proposes several directions for future research. The results of this research may be helpful for beginner researchers and experts in business failure, as they contribute to bringing clarity to this line of investigation. These results reveal all the aspects involved in business failure research, analysing its temporal and methodological characterisation, and the most prolific authors who have participated in its study (see, i.e., H. Li), leading journals (see, i.e., Expert Systems with Applications) or academic institutions that have headed the scientific analysis of this business phenomenon. Likewise, it has been possible to identify three main areas in which the research on business failure has been focused: business, management and accounting; economics, econometrics and finance; and social sciences. In addition, a complete, synthesised and organised summary of the various definitions, perspectives and research trends are presented.

An Ensemble Classifier-Based Scoring Model for Predicting Bankruptcy of Polish Companies in the Podkarpackie Voivodeship

This publication presents the methodological aspects of designing of a scoring model for an early prediction of bankruptcy by using ensemble classifiers. The main goal of the research was to develop a scoring model (with good classification properties) that can be applied in practice to assess the risk of bankruptcy of enterprises in various sectors. For the data sample, which included 1739 Polish businesses (of which 865 were bankrupt and 875 had no risk of bankruptcy), a genetic algorithm was applied to select the optimum set of 19 bankruptcy indicators, on the basis of which the classification accuracy of a number of ensemble classifier model variants (boosting, bagging and stacking) was estimated and verified. The classification effectiveness of ensemble models was compared with eight classical individual models which made use of single classifiers. A GBM-based ensemble classifier model offering superior classification capabilities was used in practice to design a scoring model, which was applied in comparative evaluation and bankruptcy risk analysis for businesses from various sectors and of different sizes from the Podkarpackie Voivodeship in 2018 (over a time horizon of up to two years). The approach applied can also be used to assess credit risk for corporate borrowers.

A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems

In recent years, a forward-looking subfield of machine learning has emerged with important applications in a variety of scientific fields. Semi-supervised learning is increasingly being recognized as a burgeoning area embracing a plethora of efficient methods and algorithms seeking to exploit a small pool of labeled examples together with a large pool of unlabeled ones in the most efficient way. Co-training is a representative semi-supervised classification algorithm originally based on the assumption that each example can be described by two distinct feature sets, usually referred to as views. Since such an assumption can hardly be met in real world problems, several variants of the co-training algorithm have been proposed dealing with the absence or existence of a naturally two-view feature split. In this context, a Static Selection Ensemble-based co-training scheme operating under a random feature split strategy is outlined regarding binary classification problems, where the type of the base ensemble learner is a soft-Voting one composed of two participants. Ensemble methods are commonly used to boost the predictive performance of learning models by using a set of different classifiers, while the Static Ensemble Selection approach seeks to find the most suitable structure of ensemble classifier based on a specific criterion through a pool of candidate classifiers. The efficacy of the proposed scheme is verified through several experiments on a plethora of benchmark datasets as statistically confirmed by the Friedman Aligned Ranks non-parametric test over the behavior of classification accuracy, F1-score, and Area Under Curve metrics.