Comparing Compound and Ordinary Diversity measures Using Decision Trees.

Comparing Compound and Ordinary Diversity measures Using Decision Trees.

An ensemble of classifiers succeeds in improving the accuracy of the whole when thecomponent classifiers are both diverse and accurate. Diversity is required to ensure that theclassifiers make uncorrelated errors. Theoretical and experimental approaches from previousresearch show very low correlatio...

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Bibliographic Details
Main Authors: Gangadhara, Kanthi, Reddy Dubbaka, Sai Anusha
Format: Others
Language:English
Published: Högskolan i Borås, Institutionen Handels- och IT-högskolan 2011
Subjects:
machine learning
ensemble
diversity
decision trees
compound diversity
classifiers
data mining
Engineering and Technology
Teknik och teknologier
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20385

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-20385

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