Learning classifiers from large databases using statistical queries

Neeraj Koul, Cornelia Caragea, Vasant Honavar, Vikas Bahirwani, Doina Caragea

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

3 Scopus citations

Abstract

1 We describe an approach to learning predictive models from large databases in settings where direct access to data is not available because of massive size of data, access restrictions, or bandwidth requirements. We outline some techniques for minimizing the number of statistical queries needed; and for efficiently coping with missing values in the data. We provide open source implementation of the decision tree and Naive bayes algorithms to demonstrate the feasibility of the proposed approach.

Original languageEnglish (US)
Title of host publicationProceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
Pages923-926
Number of pages4
DOIs
StatePublished - Dec 1 2008
Event2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 - Sydney, NSW, Australia
Duration: Dec 9 2008Dec 12 2008

Publication series

NameProceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008

Other

Other2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
CountryAustralia
CitySydney, NSW
Period12/9/0812/12/08

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Koul, N., Caragea, C., Honavar, V., Bahirwani, V., & Caragea, D. (2008). Learning classifiers from large databases using statistical queries. In Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008 (pp. 923-926). [4740577] (Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008). https://doi.org/10.1109/WIIAT.2008.366