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Several experimental studies have tested the relative merits of various supervised machine learning models. Comparisons have been made along dimensions that include model complexity, prediction accuracy, training set size, and training time. Only limited work has been done to study the effect of training set exemplar typicality on model performance. We present experimental results obtained in testing...
In this paper, we used data consisting of attributes containing financial performance information on failed and non-failed banks. We developed and tested several models using three induction-based machine learning techniques (C4.5, a backpropagation neural network and SX-WEB) and linear discriminant analysis. All models showed test set classification correctness under 74% when trained and tested with...
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