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

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 249 - 251, 26.12.2016

Murat Koklu Kadir Sabancı

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

• Witten I.H., Frank E., & Hall M.A. (2011). Data mining: practical machine learning tools and techniques. Elsevier, London.
• 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.
• 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.
• 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.
• Zhou, Y., Li, Y. & Xia, S., (2009). An improved KNN text classification algorithm based on clustering, Journal of computers, 4(3):230-237.
• Witten I.H., Frank E., & Hall M.A. (2011). Data mining: practical machine learning tools and techniques. Elsevier, London.
• 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.
• 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.
• 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.
• Zhou, Y., Li, Y. & Xia, S., (2009). An improved KNN text classification algorithm based on clustering, Journal of computers, 4(3):230-237.