Classifier Performances For Credit Risk Analysis

This work is prepared for a Master Research Thesis. The main objective of the work is gathering single classification techniques together as one unique hybrid classifier. Experiments made on different data-sets and results are compared in terms of accuracy and precision. Logistic regression, support vector machines, artificial neural networks and naive bayes approach are examined throughout the research. A hybrid model based on average weighting mechanism developed by using those single classifiers.

Autorentext

I was graduated from Isik University , Computer Engineering and Management of Information Systems departments. I completed my research on classifier performances for credit risk analysis at my Master Thesis. I will continue to work on hybrid classification approach. I am working as a remote technical support engineer at Alcatel-Lucent Turkey.