This paper introduces a method for classification by optimization of geometric shapes (COGS).
A COGS solution consists of a set of convex regions, each of which contains points predominantly from one class. Two "tight" geometric representations for convex regions have been investigated. A randomized search procedure is described to minimize the number of regions for a given limit
on classification error on the training data. We also describe the use of this search procedure
to derive a classification solution. Experimental results on synthetic examples are given to illustrate and compare the results from COGS and from a decision tree classifier.
By: Vijay Iyengar, Daniel Brand, Murray Campbell, and Philip Heidelberger
Published in: RC21486 in 1999
LIMITED DISTRIBUTION NOTICE:
This Research Report is available. This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). I have read and understand this notice and am a member of the scientific community outside or inside of IBM seeking a single copy only.