Name
Abstract | ||
|---|---|---|
This paper proposes a predictive model to handle customer insolvency in advance for large mobile telecommunication companies for the purpose of minimizing their losses while preserving an overall satisfaction of the customers which may have important consequences on the quality and on the consume return of the operations. A new mathematical formulation taking into consideration a set of business rules and the satisfaction of the customers is proposed. However, the customer insolvency is defined to be a classification problem since our main purpose is to categorize the customer in one of the two classes: potentially insolvent or potentially solvent. Therefore, a model with precise business prediction using the knowledge discovery and Data Mining techniques on an enormous heterogeneous and noisy data is proposed. A fuzzy approach to evaluate and analyze the customer behavior leading to segment them into groups that provide better understanding of customers is developed. These groups with many other significant variables feed into a classification algorithm based on Rough fuzzy Sets technique to classify the customers. A real case study is considered here, followed by analysis and comparison of the results for the reason to select the best classification model that maximizes the accuracy for insolvent customers and minimizes the error rate in the misclassification of solvent customers. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.4018/ijdsst.2014070101 | International Journal of Decision Support System Technology |
Keywords | Field | DocType |
Classification, Customer Insolvency, Decision Tree, Fuzzy Logic, Naive Bayesian, Neural Network, Predictive Data Mining, Rough Sets | Customer intelligence,Data mining,Fuzzy classification,Computer science,Fuzzy logic,Insolvency,Fuzzy set,Rough set,Knowledge extraction,Business rule | Journal |
Volume | Issue | ISSN |
6 | 3 | 1941-6296 |
Citations | PageRank | References |
0 | 0.34 | 23 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Walid Moudani | 1 | 11 | 2.26 |
| Grace Zaarour | 2 | 0 | 0.34 |
| Félix Mora-Camino | 3 | 41 | 12.11 |