Correcting sample selection bias in maximum entropy density estimation

Miroslav Dudík, Robert E. Schapire, Steven J. Phillips

Research output: Chapter in Book/Report/Conference proceedingConference contribution

95 Scopus citations

Abstract

We study the problem of maximum entropy density estimation in the presence of known sample selection bias. We propose three bias correction approaches. The first one takes advantage of unbiased sufficient statistics which can be obtained from biased samples. The second one estimates the biased distribution and then factors the bias out. The third one approximates the second by only using samples from the sampling distribution. We provide guarantees for the first two approaches and evaluate the performance of all three approaches in synthetic experiments and on real data from species habitat modeling, where maxent has been successfully applied and where sample selection bias is a significant problem.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference
Pages323-330
Number of pages8
StatePublished - Dec 1 2005
Event2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada
Duration: Dec 5 2005Dec 8 2005

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other2005 Annual Conference on Neural Information Processing Systems, NIPS 2005
CountryCanada
CityVancouver, BC
Period12/5/0512/8/05

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Dudík, M., Schapire, R. E., & Phillips, S. J. (2005). Correcting sample selection bias in maximum entropy density estimation. In Advances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference (pp. 323-330). (Advances in Neural Information Processing Systems).