TITLE:
Machine Learning Approaches to Predict Default of Credit Card Clients
AUTHORS:
Ruilin Liu
KEYWORDS:
Machine Learning, Feedforward Neural Network, Long Short-Term Memory, Dropout
JOURNAL NAME:
Modern Economy,
Vol.9 No.11,
November
19,
2018
ABSTRACT: This paper compares traditional machine learning
models, i.e. Support Vector Machine,
k-Nearest Neighbors, Decision Tree and Random Forest, with Feedforward Neural
Network and Long Short-Term Memory. We observe that the two neural networks
achieve higher accuracies than traditional models. This paper also tries to
figure out whether dropout can improve accuracy of neural networks. We observe
that for Feedforward Neural Network, applying dropout can lead to better
performances in certain cases but worse performances in others. The influence
of dropout on LSTM models is small. Therefore, using dropout does not guarantee
higher accuracy.