A Genetic Algorithm-Based Heterogeneous Random Subspace Ensemble Model for Bankruptcy Prediction

Ensemble classification involves combining multiple classifiers to obtain more accurate predictions than those obtained using individual models. Ensemble techniques are known to be very useful in improving the generalization ability of a classifier. The random subspace ensemble technique is a simple but effective method of constructing ensemble classifiers, in which some features are randomly drawn from all features of each classifier in the ensemble. Recently, ensemble techniques have been successfully applied to bankruptcy prediction, but few studies have incorporated heterogeneous random subspace models for bankruptcy prediction. A heterogeneous ensemble is a set of base classifiers created using different algorithms, while a homogeneous ensemble uses only one algorithm to create base classifiers. In this study, we applied a heterogeneous random subspace model to the bankruptcy prediction problem. We also developed a method for optimizing the heterogeneous random subspace ensemble model, using a genetic algorithm to optimize its classifier subsets. We applied the proposed model to a bankruptcy prediction problem using a real dataset from Korean companies. The experimental results confirmed that the proposed model outperformed other models.