Models of corporate bankruptcy prediction with the ensemble of classifiers algorithm
Classification problems represent the group of machine learning methods where each instance is associated with a certain category or label. An individual classifier like Neural Networks, or Decision Trees is conventionally trained on a pre-marked or processed data set. Depending on the parameters distributions the data sets may feature issues when all the indicators are not learned eficiently by such a classifier, and this results in an inconsistent performance on the test sets. Ensemble classifiers denote a set of individual classifiers algorithms that are simultaneously trained in a classification problem. The paper aim is twofold. We present an ensemble of classifiers approach with a high predictive power for the Russian trade-related companies bankruptcy prediction. At the first stage we split the data into a train set (70%) and a test set (30%). At the second stage the precision of standard algorithms is measured as applied to the empirical indicators of the data. The algorithms are trained and tested, and then compared via the performance metrics. The standard algorithms include: random forest, decision trees and the modifications: the chi-square automatic interaction detection (CHAID), classification and regression trees (CRT, C5), Quick, Unbiased, Efficient, Statistical Tree (QUEST), discriminant analysis LDA, support vector algorithms (LSVM, SVM), neural networks (multilayer and radial). Based on the ROC-curve metrics and the prediction ability of the algorithms we select the most efficient methods that form the ensemble of classifiers algorithm. The empirical data set included 713 trade companies (334 - known bankrupts). The results feature the efficiency of the ensemble of classifiers algorithms based on the simple voting (the precision metric outperforms the one of the other individual algorithms, e.g. random forest, SVM, Logit). We also show that including the macroeconomic factors improves the prediction power of almost all studied algorithms by at least 8%. Given that, more sophisticated variations of the classifiers such as multilayer neural networks and random forests demonstrate higher precision and recall with the external variables employed in the training process.