A logistic regression method for cost sensetive active learning

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

Abstract

Direct marketing involves offering a product or service to a carefully selected group of customers, the ones expected to render the most profits. Active learning is a data mining policy which actively selects unlabeled instances for labeling. In this research our goal is to construct a model that minimizes the net acquisition cost of selection of instances for labeling and at the same time maximizes the net profit gained from approaching selected customers. We present a new framework which combines a cost-sensitive active learning algorithm with a logistic regression classifier. We evaluated the framework on two benchmark datasets. The results appear encouraging.

Original languageEnglish
Title of host publication2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2008
Pages707-710
Number of pages4
DOIs
StatePublished - 1 Dec 2008
Event2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2008 - Eilat, Israel
Duration: 3 Dec 20085 Dec 2008

Publication series

NameIEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings

Conference

Conference2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel, IEEEI 2008
Country/TerritoryIsrael
CityEilat
Period3/12/085/12/08

Keywords

  • Active learning
  • Cost sensitive learning
  • Logistic regression

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