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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 9
  3. Swarm Intelligence : Volume 9, Issue 4, December 2015
  4. An ant colony-based semi-supervised approach for learning classification rules
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Swarm Intelligence : Volume 11
Swarm Intelligence : Volume 10
Swarm Intelligence : Volume 9
Swarm Intelligence : Volume 9, Issue 4, December 2015
Learning neural network structures with ant colony algorithms
On the design of generalist strategies for swarms of simulated robots engaged in a task-allocation scenario
Critical considerations on angle modulated particle swarm optimisers
An ant colony-based semi-supervised approach for learning classification rules
Swarm Intelligence : Volume 9, Issue 2-3, September 2015
Swarm Intelligence : Volume 9, Issue 1, March 2015
Swarm Intelligence : Volume 8
Swarm Intelligence : Volume 7
Swarm Intelligence : Volume 6
Swarm Intelligence : Volume 5
Swarm Intelligence : Volume 4
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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An ant colony-based semi-supervised approach for learning classification rules

Content Provider SpringerLink
Author Albinati, Julio Oliveira, Samuel E. L. Otero, Fernando E. B. Pappa, Gisele L.
Copyright Year 2015
Abstract Semi-supervised learning methods create models from a few labeled instances and a great number of unlabeled instances. They appear as a good option in scenarios where there is a lot of unlabeled data and the process of labeling instances is expensive, such as those where most Web applications stand. This paper proposes a semi-supervised self-training algorithm called Ant-Labeler. Self-training algorithms take advantage of supervised learning algorithms to iteratively learn a model from the labeled instances and then use this model to classify unlabeled instances. The instances that receive labels with high confidence are moved from the unlabeled to the labeled set, and this process is repeated until a stopping criteria is met, such as labeling all unlabeled instances. Ant-Labeler uses an ACO algorithm as the supervised learning method in the self-training procedure to generate interpretable rule-based models—used as an ensemble to ensure accurate predictions. The pheromone matrix is reused across different executions of the ACO algorithm to avoid rebuilding the models from scratch every time the labeled set is updated. Results showed that the proposed algorithm obtains better predictive accuracy than three state-of-the-art algorithms in roughly half of the datasets on which it was tested, and the smaller the number of labeled instances, the better the Ant-Labeler performance.
Starting Page 315
Ending Page 341
Page Count 27
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 9
Issue Number 4
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2015-11-26
Publisher Place New York
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Semi-supervised learning Self-training Ant colony optimization Classification rules Artificial Intelligence (incl. Robotics) Computer Systems Organization and Communication Networks ApplicationMathematics/Computational Methods of Engineering Communications Engineering, Networks Computer Communication Networks
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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