The use of hybrid manifold learning and support vector machines in the prediction of business failure

Abstract

The prediction of business failure is an important and challenging issue that has served as the impetus for many academic studies over the past three decades. This paper proposes a hybrid manifold learning approach model which combines both isometric feature mapping (ISOMAP) algorithm and support vector machines (SVM) to predict the failure of firms based on past financial performance data. By making use of the ISOMAP algorithm to perform dimension reduction, is then utilized as a preprocessor to improve business failure prediction capability by SVM. To create a benchmark, we further compare principal component analysis (PCA) and SVM with our proposed hybrid approach. Analytic results demonstrate that our hybrid approach not only has the best classification rate, but also produces the lowest incidence of Type II errors, and is capable of achieving an improved predictive accuracy and of providing guidance for decision makers to detect and prevent potential financial crises in the early stages.