TY - GEN
T1 - Learning classifiers from large databases using statistical queries
AU - Koul, Neeraj
AU - Caragea, Cornelia
AU - Honavar, Vasant
AU - Bahirwani, Vikas
AU - Caragea, Doina
PY - 2008
Y1 - 2008
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=62949221087&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62949221087&partnerID=8YFLogxK
U2 - 10.1109/WIIAT.2008.366
DO - 10.1109/WIIAT.2008.366
M3 - Conference contribution
AN - SCOPUS:62949221087
SN - 9780769534961
T3 - Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
SP - 923
EP - 926
BT - Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
T2 - 2008 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2008
Y2 - 9 December 2008 through 12 December 2008
ER -