By Topic

Fast instance selection hybrid algorithm adapted to large data sets

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Frédéric Ros ; Institut Prisme (Orleans University), France ; Rachid Harba

This paper investigates a new hybrid algorithm for instance selection adapted to large databases. The key idea is to apply condensation algorithms to only small sets and useful patterns to reduce computation cost. The initial population is divided into “meta strata” resulting from the union of strata randomly generated. Interesting patterns are resulting from a reference “meta stratum” and are partitioned in clusters. For each “meta stratum” and each cluster, influencing patterns are selected on the basis of a 1-nn procedure. The sets of instances determined from all “meta strata” provide the final set. Experiments performed with various data sets are revealing the effectiveness and adequacy of the proposed approach.

Published in:

Soft Computing and Pattern Recognition (SoCPaR), 2011 International Conference of

Date of Conference:

14-16 Oct. 2011