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Comparing Methods for Generating Diverse Ensembles of Artificial Neural Networks
Högskolan i Borås, Institutionen Handels- och IT-högskolan.ORCID-id: 0000-0003-0274-9026
Högskolan i Borås, Institutionen Handels- och IT-högskolan.ORCID-id: 0000-0003-0412-6199
2010 (engelsk)Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

It is well-known that ensemble performance relies heavily on sufficient diversity among the base classifiers. With this in mind, the strategy used to balance diversity and base classifier accuracy must be considered a key component of any ensemble algorithm. This study evaluates the predictive performance of neural network ensembles, specifically comparing straightforward techniques to more sophisticated. In particular, the sophisticated methods GASEN and NegBagg are compared to more straightforward methods, where each ensemble member is trained independently of the others. In the experimentation, using 31 publicly available data sets, the straightforward methods clearly outperformed the sophisticated methods, thus questioning the use of the more complex algorithms.

sted, utgiver, år, opplag, sider
IEEE, 2010.
Serie
CFP10IJS-DVD
Emneord [en]
ensembles, diversity, Machine Learning
HSV kategori
Identifikatorer
URN: urn:nbn:se:hj:diva-45801DOI: 10.1109/IJCNN.2010.5596763Lokal ID: 0;0;miljJAILISBN: 978-1-4244-6916-1 (tryckt)OAI: oai:DiVA.org:hj-45801DiVA, id: diva2:1348945
Konferanse
WCCI 2010 IEEE World Congress on Computational Intelligence, IJCNN 2010
Tilgjengelig fra: 2019-09-06 Laget: 2019-09-06 Sist oppdatert: 2019-09-06bibliografisk kontrollert

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