Data Mining Approach for Analyzing Call Center Performance
| dc.creator | Paprzycki, Marcin | |
| dc.creator | Abraham, Ajith | |
| dc.creator | Guo, Ruiyuan | |
| dc.date | 2004-05-05 | |
| dc.date.accessioned | 2026-07-07T03:21:12Z | |
| dc.date.available | 2026-07-07T03:21:12Z | |
| dc.description | The aim of our research was to apply well-known data mining techniques (such as linear neural networks, multi-layered perceptrons, probabilistic neural networks, classification and regression trees, support vector machines and finally a hybrid decision tree neural network approach) to the problem of predicting the quality of service in call centers; based on the performance data actually collected in a call center of a large insurance company. Our aim was two-fold. First, to compare the performance of models built using the above-mentioned techniques and, second, to analyze the characteristics of the input sensitivity in order to better understand the relationship between the perform-ance evaluation process and the actual performance and in this way help improve the performance of call centers. In this paper we summarize our findings. | |
| dc.identifier | https://arxiv.org/abs/cs/0405017 | |
| dc.identifier | http://arxiv.org/abs/cs/0405017 | |
| dc.identifier | The 17th International Conference on Industrial & Engineering Applications of Artificial Intelligence and Expert Systems, Canada, Springer Verlag, Germany, 2004 (forth coming) | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/32106 | |
| dc.subject | Artificial Intelligence | |
| dc.subject | I.2.0 | |
| dc.title | Data Mining Approach for Analyzing Call Center Performance | |
| dc.type | text |