Name
Abstract | ||
|---|---|---|
The most widespread payment model in online advertising is Cost-per-click (CPC). In this model the advertisers pay each time that a user generates a click. In order to enhance the income of CPC Advertising Networks, it is necessary to give priority to the most profitable adverts. The most important factor in the profitability of an advert is Click-through-rate (CTR), which is the probability that a user generates a click in a given advert. In this paper we find which feature selection method between PCA, RFE, Gain ratio and NSGA-II is better suited, or if otherwise, the machine learning classification methods work best without any feature selection method. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MICAI-2016.2016.00017 | 2016 Fifteenth Mexican International Conference on Artificial Intelligence (MICAI) |
Keywords | Field | DocType |
CTR-prediction,feature-selection-methods,supervised-classification-methods,CPC-advertising-networks-models | Feature selection,Computer science,Online advertising,Profitability index,Artificial intelligence,Information gain ratio,Statistical classification,Payment,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5386-7736-0 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luis Miralles Pechuán | 1 | 3 | 1.05 |
| Hiram E. Ponce | 2 | 26 | 13.63 |
| María de Lourdes Martínez-Villaseñor | 3 | 26 | 8.35 |