OPTIMIZING OBJECT CLASSIFICATION UNDER AMBIGUITY/IGNORANCE: APPLICATION TO THE CREDIT RATING PROBLEM

摘要

A nascent technique for object classification is employed to exposit the classification of US banks to their financial strength ratings, presented by the Moody's Investors Services. The classification technique primarily utilized, called CaRBS (classification and ranking belief simplex), allows for the presence of ignorance to be inherent. The modern constrained optimization method, trigonometric differential evolution (TDE), is adopted to configure a CaRBS system. Two different objective functions are considered with TDE to measure the level of optimization achieved, which utilize differently the need to reduce ambiguity and/or ignorance inherently during the optimization process. The appropriateness of the CaRBS system to analyse incomplete data is also highlighted, with no requirement to impute any missing values or remove objects with missing values inherent. Comparative results are also presented using the well-known multivariate discriminant analysis and neural network models. The findings in this study identify a novel dimension to the issue of object classification optimization, with the discernment between the concomitant notions of ambiguity and ignorance.