Classifier Performances For Credit Risk Analysis

The book "Classifier Performances for Credit Risk Analysis" by Erkan Cetiner offers a comprehensive examination of classification techniques in the field of credit risk analysis. It was written as part of a master's research project and aims to combine various individual classifiers into a unique hybrid classifier. This work analyzes different datasets and compares the results in terms of accuracy and precision. The methods examined include logistic regression, support vector machines, artificial neural networks, and the naive Bayes approach. The author develops a hybrid model based on an average weighting mechanism to combine the strengths of the individual classifiers and enhance the efficiency of credit risk analysis.