Random ensemble feature selection for land cover mapping

Anthony Gidudu, Abe T. Bolanle, Tshilidzi Marwala

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

2 Citations (Scopus)

Abstract

Random ensemble feature selection is a means through which diversity in ensemble systems is imposed by randomly selecting the features (bands) that constitute the base classifiers. This paper provides insight and discusses the interplay between the size of the resulting ensembles and the consequent classification accuracy. From the results, it was observed that classification accuracy increased more as the number of features per base classifier increases than as the number of base classifiers increases. That said however, classification accuracy was seen to increase with additional features up to a given limit beyond which increasing the number of features per base classifier did not significantly increase classification accuracy, a peaking effect probably due to Hughes phenomenon.

Original languageEnglish
Title of host publication2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Proceedings
PagesII840-II842
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009 - Cape Town, South Africa
Duration: 12 Jul 200917 Jul 2009

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Volume2

Conference

Conference2009 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2009
Country/TerritorySouth Africa
CityCape Town
Period12/07/0917/07/09

Keywords

  • Random ensemble feature selection

ASJC Scopus subject areas

  • Computer Science Applications
  • General Earth and Planetary Sciences

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