Estimation of Credit Card Customers Payment Status by Using kNN and MLP

Öz

The Default of Credit Card Clients dataset in the UCI machine learning repository was used in this study.  The credit card customers were classified if they would do payment or not (yes=1 no=0) for next month by using 23 information about them. Totally 30000 data in the dataset’s 66% was used for training and rest of them as 33% was used for tests. The Weka (Waikato Environment for Knowledge Analysis) software was used for estimation. In estimation Multilayer Perceptron (MLP) and k Nearest Neighbors (kNN) machine learning algorithms was used and success rates and error rates were calculated. With kNN estimation success rates for various number of neighborhood value was calculated one by one. The highest success rate was achieved as 80.6569% when the number of neighbor is 10. With MLP neural network model the estimation success rates was calculated when there are different number of neurons in the hidden layer of MLP. The best estimation success rate was achieved as 81.049% when there was only one neuron in the hidden layer.  MAE and RMSE values were obtained for this estimation success rate as 0.3237 and 0.388 respectively. 

Anahtar Kelimeler

• Data mining,Weka,MLP

Kaynakça

1. Witten I.H., Frank E., & Hall M.A. (2011). Data mining: practical machine learning tools and techniques. Elsevier, London.
2. Patterson, D., Liu, F., Turner, D., Concepcion, A., & Lynch, R., (2008). Performance Comparison of the Data Reduction System. Proceedings of the SPIE Symposium on Defense and Security, Mart, Orlando, FL, pp. 27-34.
3. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I.H. (2009). The WEKA Data Mining Software: An Update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18.
4. Wang, J., Neskovic, P., & Cooper, L. N., (2007). Improving nearest neighbour rule with a simple adaptive distance measure, Pattern Recognition Letters, 28(2):207-213.
5. Zhou, Y., Li, Y. & Xia, S., (2009). An improved KNN text classification algorithm based on clustering, Journal of computers, 4(3):230-237.
6. Witten I.H., Frank E., & Hall M.A. (2011). Data mining: practical machine learning tools and techniques. Elsevier, London.
7. Patterson, D., Liu, F., Turner, D., Concepcion, A., & Lynch, R., (2008). Performance Comparison of the Data Reduction System. Proceedings of the SPIE Symposium on Defense and Security, Mart, Orlando, FL, pp. 27-34.
8. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I.H. (2009). The WEKA Data Mining Software: An Update. ACM SIGKDD Explorations Newsletter, 11(1), 10–18.

9. Wang, J., Neskovic, P., & Cooper, L. N., (2007). Improving nearest neighbour rule with a simple adaptive distance measure, Pattern Recognition Letters, 28(2):207-213.
10. Zhou, Y., Li, Y. & Xia, S., (2009). An improved KNN text classification algorithm based on clustering, Journal of computers, 4(3):230-237.