Online adaptive policies for ensemble classifiers

• Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, we attempt to train and combine the base classifiers using an adaptive policy. This policy is learnt through a Q-learning inspired technique. Its effectiveness for an essentially supervised task is demonstrated by experimental results on several UCI benchmark databases. © 2005 Elsevier B.V. All rights reserved.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Keyword

Q-learning

Mixture of experts

Ensembles

Neural networks

Boosting

Reinforcement learning

Supervised learning

Publication and Content Type

art (subject category)

ref (subject category)