Bankruptcy Prediction with Missing Data
Bankruptcy prediction has been widely studied as a binary classification problem using financial ratios methodologies. When calculating the ratios, it is common to confront missing data problem. Thus, this paper proposes a classification method Ensemble Nearest Neighbors (ENN) to solve it. ENN uses different nearest neighbors as ensemble classifiers then make a linear combination of them. Instead of choosing k in original k-Nearest Neighbors algorithm, ENN provides weights to each classifier which makes the method more robust. Moreover, using a adapted distance metric, ENN can be used directly for incomplete data.