Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Clustering Algorithms
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
From Recombination of Genes to the Estimation of Distributions I. Binary Parameters
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Clustering by soft-constraint affinity propagation
Bioinformatics
Cancer Profiles by Affinity Propagation
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Learning factorizations in estimation of distribution algorithms using affinity propagation
Evolutionary Computation
Protein Folding in Simplified Models With Estimation of Distribution Algorithms
IEEE Transactions on Evolutionary Computation
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Tumor classification based on gene expression data can be applied to set appropriate medical treatment according to the specific tumor characteristics. In this paper we propose the use of estimation of distribution algorithms (EDAs) to enhance the performance of affinity propagation (AP) in classification problems. AP is an efficient clustering algorithm based on message-passing methods and which automatically identifies exemplars of each cluster. We introduce an EDA-based procedure to compute the preferences used by the AP algorithm. Our results show that AP performance can be notably improved by using the introduced approach. Furthermore, we present evidence that classification of new data is improved by employing previously identified exemplars with only minor decrease in classification accuracy.