Ensemble Learning for Free with Evolutionary Algorithms ?

Details Ensemble Learning for Free with Evolutionary Algorithms ? Ensemble Learning for Free with Evolutionary Algorithms ?

2007-04-30

arxiv.org/abs/0704.3905v1

Dans GECCO (2007)

Computer Science

Artificial Intelligence

Scientific paper

Evolutionary Learning proceeds by evolving a population of classifiers, from which it generally returns (with some notable exceptions) the single best-of-run classifier as final result. In the meanwhile, Ensemble Learning, one of the most efficient approaches in supervised Machine Learning for the last decade, proceeds by building a population of diverse classifiers. Ensemble Learning with Evolutionary Computation thus receives increasing attention. The Evolutionary Ensemble Learning (EEL) approach presented in this paper features two contributions. First, a new fitness function, inspired by co-evolution and enforcing the classifier diversity, is presented. Further, a new selection criterion based on the classification margin is proposed. This criterion is used to extract the classifier ensemble from the final population only (Off-line) or incrementally along evolution (On-line). Experiments on a set of benchmark problems show that Off-line outperforms single-hypothesis evolutionary learning and state-of-art Boosting and generates smaller classifier ensembles.

Affiliated with

Also associated with

No associations

Rating

Ensemble Learning for Free with Evolutionary Algorithms ? does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Ensemble Learning for Free with Evolutionary Algorithms ?, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Ensemble Learning for Free with Evolutionary Algorithms ? will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-415227