Name
Abstract | ||
|---|---|---|
The present paper explores the possible application of a new ensemble model. The model, which is based on multiple SVM classifiers, is employed to address churner identification problems in the mobile telecommunication industry, a sector in which the role of customer retention program becomes increasingly important due to its very competitive business environment. In particular, the current study introduces a uniformly subsampled ensemble (USE) model of SVM classifiers, not only to reduce the computational complexity of large-scale data, but also to boost the reliability and accuracy of calibrated models on data sets with highly skewed class distributions. According to our experiments, the performance of the USE SVM model is superior compared to all single and ensemble models. It is more scalable than well-known ensemble models as well. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.eswa.2012.01.203 | Expert Syst. Appl. |
Keywords | Field | DocType |
svm classifier,well-known ensemble model,new ensemble model,subsampled ensemble,use svm model,large-scale data,churn management,churner identification problem,ensemble model,multiple svm classifier,support vector machine | Customer retention,Data mining,Data set,Ensemble forecasting,Computer science,Support vector machine,Artificial intelligence,Ensemble learning,Machine learning,Mobile telephony,Computational complexity theory,Scalability | Journal |
Volume | Issue | ISSN |
39 | 15 | 0957-4174 |
Citations | PageRank | References |
6 | 0.49 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Namhyoung Kim | 1 | 35 | 2.68 |
| Kyu-Hwan Jung | 2 | 82 | 4.82 |
| Yong Seog Kim | 3 | 112 | 11.11 |
| Jaewook Lee | 4 | 72 | 8.87 |