PROCEEDINGS IPMU '08
A genetic approach for training diverse classifier ensembles
David Gacquer, François Delmotte, Veronique Delcroix, Sylvain Piechowiak.
Classification is an active topic of
Machine Learning. The most recent
achievements in this domain suggest
using ensembles of learners instead
of a single classifier to improve
classification accuracy. Comparisons
between Bagging and Boosting
show that classifier ensembles perform
better when their members exhibit
diversity, that is commit different
errors. This paper proposes
a genetic algorithm to design classifier
ensembles, using a fitness function
based on both accuracy and diversity.
The proposed implementation
has been run on several UCI
Machine Learning datasets and compared
to the performances obtained
with bagging algorithm and a single
classifier of the same nature.
 | PDF full paper |