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Comparing Compound and Ordinary Diversity measures Using Decision Trees.
University of Borås, School of Business and IT.
University of Borås, School of Business and IT.
2011 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

An ensemble of classifiers succeeds in improving the accuracy of the whole when the component classifiers are both diverse and accurate. Diversity is required to ensure that the classifiers make uncorrelated errors. Theoretical and experimental approaches from previous research show very low correlation between ensemble accuracy and diversity measure. Introducing Proposed Compound diversity functions by Albert Hung-Ren KO and Robert Sabourin, (2009), by combining diversities and performances of individual classifiers exhibit strong correlations between the diversities and accuracy. To be consistent with existing arguments compound diversity of measures are evaluated and compared with traditional diversity measures on different problems. Evaluating diversity of errors and comparison with measures are significant in this study. The results show that compound diversity measures are better than ordinary diversity measures. However, the results further explain evaluation of diversity of errors on available data.

Place, publisher, year, edition, pages
University of Borås/School of Business and Informatics , 2011.
Series
Magisteruppsats ; 2010MI16
Keywords [en]
machine learning, ensemble, diversity, decision trees, compound diversity, classifiers
Keywords [sv]
data mining
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hb:diva-20385Local ID: 2320/7969OAI: oai:DiVA.org:hb-20385DiVA, id: diva2:1312319
Note
Program: Magisterutbildning i informatikAvailable from: 2019-04-30 Created: 2019-04-30

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CiteExportLink to record
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Citation style
  • apa
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  • nn-NB
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Output format
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