Investigation of diversity and accuracy in ensemble of classifiers using bayesian decision rules

Man Sing Wong, Wai Yeung Yan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Multiple Classifier System (MCS) has attracted increasing interest in the field of pattern recognition and machine learning where this technique has also been introduced in remote sensing. The importance of classifier diversity in MCS has been raised recently; nevertheless, only a few of the researches have been studied in land cover classification problem. In this paper, a SPOT IV satellite image covering the Hong Kong Island and Kowloon Peninsula with six land cover classes were classified with four base classifiers: Minimum Distance Classifier, Maximum Likelihood Classifier, Mahalanobis Classifier and K-Nearest Neighbor Classifier. Same training and testing data sets were applied throughout the experiments and five Bayesian decision rules, including product rule, sum rule, max rule, min rule, and median rule, were utilized to construct different ensemble of classifiers. Performance of MCS was measured using the overall accuracy and kappa statistics, and three statistical tests including McNemar's Test, Cochran's Q Test and F-Test were introduced to examine the dependence of the classification results. The experimental comparison reveals that i. increasing number of base classifiers may not improve the overall accuracy in MCS, ii. significant diversity in base classifiers cannot enhance the overall performance and vice versa. These findings are noted with the condition in using the same data set and the same training set.
Original languageEnglish
Title of host publication2008 International Workshop on Earth Observation and Remote Sensing Applications, EORSA
DOIs
Publication statusPublished - 25 Dec 2008
Event2008 International Workshop on Earth Observation and Remote Sensing Applications, EORSA - Beijing, China
Duration: 30 Jun 20082 Jul 2008

Conference

Conference2008 International Workshop on Earth Observation and Remote Sensing Applications, EORSA
CountryChina
CityBeijing
Period30/06/082/07/08

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)
  • Computer Networks and Communications

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