Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10361750 | Pattern Recognition Letters | 2005 | 10 Pages |
Abstract
This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Vito Di Gesù, Giosuè Lo Bosco,