Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Ensembling neural networks: many could be better than all
Artificial Intelligence
Artificial Intelligence in Medicine
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
A hybrid immune-estimation distribution of algorithm for mining thyroid gland data
Expert Systems with Applications: An International Journal
Impact of multiple clusters on neural classification of ROIs in digital mammograms
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Relevant gene selection using normalized cut clustering with maximal compression similarity measure
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Novel swarm optimization for mining classification rules on thyroid gland data
Information Sciences: an International Journal
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Micorarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes and a few hundreds of samples. Such extreme asymmetry between the dimensionality of genes and samples presents several challenges to conventional clustering and classification methods. In this paper, a novel ensemble method is proposed. Firstly, in order to extract useful features and reduce dimensionality, different feature selection methods such as correlation analysis, Fisher-ratio is used to form different feature subsets. Then a pool of candidate base classifiers is generated to learn the subsets which are re-sampling from the different feature subsets with PSO (Particle Swarm Optimization) algorithm. At last, appropriate classifiers are selected to construct the classification committee using EDAs (Estimation of Distribution Algorithms). Experiments show that the proposed method produces the best recognition rates on four benchmark databases.