A Novel Approach to Classification in Financial Applications

Modern methods for classification analysis involve processes for “learning” to correctly assign elements of a data set to certain classes. In many settings, the learning processes are supervised; i.e. the classes that the training data belong to are known in advance. In many other settings, however, the classes are not known a priori, and a process utilizing unsupervised learning is necessary. We present a novel, two-stage unsupervised learning methodology for the classification problem. Stage one consists of a special clustering method based on a quadratic, unconstrained optimization model that finds optimal classes for the data. Stage two makes use of enhanced mathematical programming models for classifying the data into the optimal classes found during stage one. A significant advantage of our approach, as demonstrated by computational testing, is the ability to yield more meaningful classifications than previously achieved in a variety of settings. We report the outcome of training and testing our method on various data sets from the data mining literature, with specific applications in finance. The comparative results disclose the effectiveness and versatility of the approach, and its merit as a tool for modeling and solving practical problems