Abstract
Hybrid classification approaches on credit domain are widely used to obtain valuable information about customer behaviours. Single classification algorithms such as neural networks, support vector machines and regression analysis have been used since years on related area. In this paper, we propose hybrid classification approaches, which try to combine several classifiers and ensemble learners to boost accuracy on classification results. We worked with two credit datasets, German dataset which is a public dataset and a Turkish Corporate Bank dataset. The goal of using such diverse datasets is to search for generalization ability of proposed model. Results show that feature selection plays a vital role on classification accuracy, hybrid approaches which shaped with ensemble learners outperform single classification techniques and hybrid approaches which consists SVM has better accuracy performance than other hybrid approaches.
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Acknowledgement
The work of V.C. Gungor was supported by the Turkish National Academy of Sciences Distinguished Young Scientist Award Program (TUBA-GEBIP) under Grand no. V.G./TUBA-GEBIP/2013-14.
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Cetiner, E., Gungor, V.C., Kocak, T. (2018). Evaluation of Hybrid Classification Approaches: Case Studies on Credit Datasets. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_6
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