A case-based reasoning driven ensemble learning paradigm for financial distress prediction with missing data

Abstract

Financial distress prediction is often accompanied by missing sample data. For this purpose, a novel case-based reasoning (CBR) driven ensemble learning paradigm is proposed for financial distress prediction with missing data. In the proposed paradigm, three main stages, CBR-driven missing data imputation, CBR-driven single classifiers prediction, and CBR-driven ensemble result output, are involved. In the first stage, the CBR-driven missing data imputation method is used to fill in missing values in the initial dataset. Second, three different CBR-driven single classification models are constructed using Manhattan distance, Euclidean distance, and cosine distance to predict financial distress, respectively. In the final stage, the weighted majority voting strategy is used to ensemble prediction results of the CBR-driven single classification models to improve prediction accuracy and robustness. For illustration and verification, the experiments on datasets with different missing rates of six Chinese listed companies are performed. And corresponding results show that the proposed CBR-driven ensemble learning paradigm can effectively improve the imputation performance and increase the robustness of classification performance, indicating that the proposed CBR-driven ensemble learning paradigm can be used as a competitive solution to financial distress prediction with missing data.