Skip to main content
Kent Academic Repository

On the Relation between Dependence and Diversity in Multiple Classifier Systems

Chen, Dechang and Sirlantzis, Konstantinos and Hua, Dong and Ma, Xiaobin (2005) On the Relation between Dependence and Diversity in Multiple Classifier Systems. In: International Conference on Information Technology: Coding and Computing (ITCC'05). IEEE, pp. 134-139. ISBN 0-7695-2315-3. (doi:10.1109/ITCC.2005.214) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:8927)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided.
Official URL:
http://dx.doi.org/10.1109/ITCC.2005.214

Abstract

In this paper we investigate the issues of independence and diversity among individual classifiers participating in a multiple classifier fusion scheme. First we present a formal definition of statistically independent classifiers. Then we focus on testing the independence between two classifiers. Dependence of two classifiers leads to the conclusion that every ensemble of classifiers in which they participate is not an independent scheme. Previous studies have argued that independence of the classifiers infuses diversity in the multi-classifier system, which is directly related to improved performance. Consequently, we introduce a measure for the degree of diversity as expressed by the agreement among the classifiers' outputs in such an ensemble. A number of examples drawn from diverse domains in pattern recognition are also given to illustrate the relation between classifier dependence and diversity estimation. Our results suggest the measurement of the classifiers' decisions agreement as an informative measure of the strength of association among dependent classifiers.

Item Type: Book section
DOI/Identification number: 10.1109/ITCC.2005.214
Uncontrolled keywords: classifier combination; independent classifiers; diversity; Kappa statistic; Pearson's chi-square statistic
Subjects: T Technology > TK Electrical engineering. Electronics. Nuclear engineering > TK7800 Electronics > TK7880 Applications of electronics > TK7885 Computer engineering. Computer hardware
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Konstantinos Sirlantzis
Date Deposited: 15 Aug 2009 17:06 UTC
Last Modified: 05 Nov 2024 09:41 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/8927 (The current URI for this page, for reference purposes)

University of Kent Author Information

Sirlantzis, Konstantinos.

Creator's ORCID: https://orcid.org/0000-0002-0847-8880
CReDIT Contributor Roles:
  • Depositors only (login required):

Total unique views for this document in KAR since July 2020. For more details click on the image.