Name
Abstract | ||
|---|---|---|
High-frequency numbers refer to phone numbers that exceed a certain number of calls every day. These numbers are mainly dividedinto two categories: advertising harassment and fast-food delivery. One of the most important jobs in telecommunication business is to automatically classify the high-frequency numbers into these two categories using machine learning techniques in the server side.However, the number of advertising harassment is much higher than that of fast-food delivery, leading to a very low recognition accuracy of fast-food delivery numbers. Therefore, this paper employs the Diversified Sensitivity-based Undersampling (DSUS) to handle the class imbalance issue in the classification process and to improve the recognition accuracy of the fast-food delivery numbers. Experimental studies show that, compared to two opponent methods, the DSUS yields a higher recognition accuracy rate of the fast-food delivery numbers. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICMLC.2018.8526943 | 2018 International Conference on Machine Learning and Cybernetics (ICMLC) |
Keywords | Field | DocType |
High frequency phone numbers,Imbalanced classification,DSUS,RBF neural network,Advertising harassment,Fast-food delivery | Computer science,Server,Undersampling,Phone,Artificial intelligence,Statistical classification,Machine learning,Harassment | Conference |
Volume | ISSN | ISBN |
1 | 2160-133X | 978-1-5386-5215-2 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhujie Lin | 1 | 0 | 0.34 |
| Jianjun Zhang | 2 | 9 | 3.48 |
| Wing W. Y. Ng | 3 | 528 | 56.12 |