The use of entropy to measure structural diversity

L. Masisi, V. Nelwamondo, T. Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

40 Citations (Scopus)

Abstract

In this paper entropy based methods are compared and used to measure structural diversity of an ensemble of 21 classifiers. This measure is mostly applied in ecology, whereby species counts are used as a measure of diversity. The measures used were Shannon entropy, Simpsons and the Berger Parker diversity indexes. As the diversity indexes increased so did the accuracy of the ensemble. An ensemble dominated by classifiers with the same structure produced poor accuracy. Uncertainty rule from information theory was also used to further define diversity. Genetic algorithms were used to find the optimal ensemble by using the diversity indices as the cost function. The method of voting was used to aggregate the decisions.

Original languageEnglish
Title of host publicationICCC 2008 - IEEE 6th International Conference on Computational Cybernetics, Proceedings
Pages41-45
Number of pages5
DOIs
Publication statusPublished - 2008
Externally publishedYes
EventICCC 2008 - IEEE 6th International Conference on Computational Cybernetics - Stara Lesna, Slovakia
Duration: 27 Nov 200829 Nov 2008

Publication series

NameICCC 2008 - IEEE 6th International Conference on Computational Cybernetics, Proceedings

Conference

ConferenceICCC 2008 - IEEE 6th International Conference on Computational Cybernetics
Country/TerritorySlovakia
CityStara Lesna
Period27/11/0829/11/08

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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