The Strength of Weak Learnability
Machine Learning
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue
Artificial Intelligence in Medicine
Lung cancer cell identification based on artificial neural network ensembles
Artificial Intelligence in Medicine
Gene Selection for Microarray Data by a LDA-Based Genetic Algorithm
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
Cancer classification using microarray and layered architecture genetic programming
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Ensemble gene selection by grouping for microarray data classification
Journal of Biomedical Informatics
Computers in Biology and Medicine
Ensemble gene selection for cancer classification
Pattern Recognition
Gene selection and cancer microarray data classification via mixed-integer optimization
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Time series gene expression data classification via L1-norm temporal SVM
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Hybrid feature selection by combining filters and wrappers
Expert Systems with Applications: An International Journal
Municipal revenue prediction by ensembles of neural networks and support vector machines
WSEAS Transactions on Computers
A new combined filter-wrapper framework for gene subset selection with specialized genetic operators
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Hybrid genetic algorithm-neural network: Feature extraction for unpreprocessed microarray data
Artificial Intelligence in Medicine
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Transductive cost-sensitive lung cancer image classification
Applied Intelligence
Flexible case-based retrieval for comparative genomics
Applied Intelligence
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
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Molecular level diagnostics based on microarray technologies can offer the methodology of precise, objective, and systematic cancer classification. Genome-wide expression patterns generally consist of thousands of genes. It is desirable to extract some significant genes for accurate diagnosis of cancer because not all genes are associated with a cancer. In this paper, we have used representative gene vectors that are highly discriminatory for cancer classes and extracted multiple significant gene subsets based on those representative vectors respectively. Also, an ensemble of neural networks learned from the multiple significant gene subsets is proposed to classify a sample into one of several cancer classes. The performance of the proposed method is systematically evaluated using three different cancer types: Leukemia, colon, and B-cell lymphoma.