Feature selection methods evaluation for CTR estimation

Luis Miralles-Pechuán, Hiram Ponce, Lourdes Martínez-Villaseñor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The most widespread payment model in online advertising is Cost-per-click (CFC). 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.

Original languageEnglish
Title of host publicationProceedings of a Special Session - 15th Mexican International Conference on Artificial Intelligence
Subtitle of host publicationAdvances in Artificial Intelligence, MICAI 2016
EditorsGrigori Sidorov, Oscar Herrera Alcantara, Sabino Miranda Jimenez, Obdulia Pichardo Lagunas
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages57-62
Number of pages6
ISBN (Electronic)9781538677353
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event15th Mexican International Conference on Artificial Intelligence, MICAI 2016 - Cancun, Quintana Roo, Mexico
Duration: 23 Oct 201629 Oct 2016

Publication series

NameProceedings of a Special Session - 15th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence, MICAI 2016

Conference

Conference15th Mexican International Conference on Artificial Intelligence, MICAI 2016
Country/TerritoryMexico
CityCancun, Quintana Roo
Period23/10/1629/10/16

Keywords

  • CPC advertising networks models
  • CTR prediction
  • Feature selection methods
  • Supervised classification methods

Fingerprint

Dive into the research topics of 'Feature selection methods evaluation for CTR estimation'. Together they form a unique fingerprint.

Cite this