ACADSTAFF UGM
| Title | : | Performance Evaluation of Backpropagation, Extreme Gradient Boosting, Feedforward Network for Classification of Customer Deposits Subscription |
| Author | : |
TRIS DIANASARI (1) Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc. (2) |
| Date | : | 1 2021 |
| Keyword | : | Backpropagation, Neural Network, Extreme Gradient Boosting, Deep Learning, Accuracy, Classification Backpropagation, Neural Network, Extreme Gradient Boosting, Deep Learning, Accuracy, Classification |
| Abstract | : | Neural Network is a method often used to predict. The most popular technique is the Neural Network Backpropagation algorithm. However, the Backpropagation algorithm has some weaknesses. It took too long to be convergent and it has minimum local problems that make artificial Neural Networks often get stuck at the local minimum. Deep Neural Network is an Artificial Neural Network that has many layers, generally more than 3 layers (input layer, N hidden layers, output layer). Mxnet is one of the developed algorithms from deep Neural Networks that have the advantage of producing better accuracy. Boosting is an ensemble family that includes many algorithms. Xgboost is a more efficient and scalable version of the Gradient Boosting Machine. This case study using data is related to direct marketing campaigns of a Portuguese banking institution in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. From the results of the analysis with 3 different methods, it can be concluded that the method with the best accuracy in the case study of additional Bank data is the Extreme Gradient Boosting Method, followed by the Deep Learning Feedforward Network method, and finally the Neural Network. |
| Group of Knowledge | : | Statistik |
| Original Language | : | English |
| Level | : | Internasional |
| Status | : |
Published
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