Gene expression classification using optimal feature/classifier ensemble with negative correlation

Proceedings of the International Joint Conference on Neural Networks

Volumn 1, Issue , 2002, Pages 198-203

  Ryu, Jungwon ^a   Cho, Sung Bae ^a  

^a Yonsei University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DNA; GENES; ONCOGENIC VIRUSES; OPTIMIZATION;

CANCER DETECTION; MICROARRAYS;

NEURAL NETWORKS;

EID: 0036075617     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
2. Interpreting patterns of gene expression with self-organizing map: Methods and application to hematopoietic differentiation
3. Yeast microarrays for genome wide parallel genetic and gene expression analysis
4. Exploring the metabolic and genetic control of gene expression on a genomic scale
5. Cluster analysis and display of genome-wide expression patterns
6. Towards optimal feature and classifier for gene expression classification of cancer
7. Modular learning
8. Cascaded back-propagation on dynamic connectionist networks
9. Initial experiments of constructing domains of expertise and hierarchies in connectionists systems
10. The Meta-Pi network: Building distributed knowledge representations for robust multisource pattern recognition
11. Synergy of clustering multiple backpropagation networks
12. Democracy in neural nets: Voting schemes for classification
13. Ensemble learning via negative correlation
14. Evolutionary ensembles with negative correlation learning
15. An introduction to computing with neural nets
16. Gender classification with support vector machines
17. Gender recognition of human behaviors using neural network ensembles