Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem

2006 ◽  
Vol 7 (4) ◽  
pp. 720-747 ◽  
Author(s):  
Yueh-Min Huang ◽  
Chun-Min Hung ◽  
Hewijin Christine Jiau
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Assessment Task ◽  
Imbalance Problem ◽  
Mining Methods

Data Mining for Potential Customer Segmentation in the Marketing Bank Dataset

2021 ◽  
Vol 9 (1) ◽  
pp. 25
Author(s):  
Maulida Ayu Fitriani ◽  
Dany Candra Febrianto
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Direct Marketing ◽  
Class Imbalance ◽  
Processing Technique ◽  
Customer Segmentation ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Mining Methods ◽  
Bank Marketing

Direct marketing is an effort made by the Bank to increase sales of its products and services, but the Bank sometimes has to contact a customer or prospective customer more than once to ascertain whether the customer or prospective customer is willing to subscribe to a product or service. To overcome this ineffective process several data mining methods are proposed. This study compares several data mining methods such as Naïve Bayes, K-NN, Random Forest, SVM, J48, AdaBoost J48 which prior to classification the SMOTE pre-processing technique was done in order to eliminate the class imbalance problem in the Bank Marketing dataset instance. The SMOTE + Random Forest method in this study produced the highest accuracy value of 92.61%.

Sign in / Sign up

Export Citation Format

Share Document