|
| This Article | ||
| ||
| Share | ||
| Bibliographic References | ||
| Add to: | ||
 Digg  Furl  Spurl  Blink  Simpy  Google  Del.icio.us  Y!MyWeb | ||
| Search | ||
| ||
2009 International Conference on Machine Learning and Applications
Ensemble Possibilistic K-NN for Functional Clustering of Gene Expression Profiles in Human Cancers Challenge
Miami Beach, Florida
December 13-December 15
ISBN: 978-0-7695-3926-3
| ASCII Text | x | ||
| Aleksey Fadeev, Oualid Missaoui, Hichem Frigui, "Ensemble Possibilistic K-NN for Functional Clustering of Gene Expression Profiles in Human Cancers Challenge," Machine Learning and Applications, Fourth International Conference on, pp. 439-442, 2009 International Conference on Machine Learning and Applications, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/ICMLA.2009.123, author = {Aleksey Fadeev and Oualid Missaoui and Hichem Frigui}, title = {Ensemble Possibilistic K-NN for Functional Clustering of Gene Expression Profiles in Human Cancers Challenge}, journal ={Machine Learning and Applications, Fourth International Conference on}, volume = {0}, year = {2009}, isbn = {978-0-7695-3926-3}, pages = {439-442}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICMLA.2009.123}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Machine Learning and Applications, Fourth International Conference on TI - Ensemble Possibilistic K-NN for Functional Clustering of Gene Expression Profiles in Human Cancers Challenge SN - 978-0-7695-3926-3 SP439 EP442 A1 - Aleksey Fadeev, A1 - Oualid Missaoui, A1 - Hichem Frigui, PY - 2009 VL - 0 JA - Machine Learning and Applications, Fourth International Conference on ER - | |||
This paper describes the Ensemble Possibilistic K-NN algorithm for classification of gene expression profiles into three major cancer categories. In fact, a modification of forward feature selection is proposed to identify relevant feature subsets allowing for multiple possibilistic K-nearest neighbors (pK-NNs) rule experts. First, individual features are ranked according to their performance on training data and subsets of features identified using greedy approach. Each subset has significantly lower dimensionality than the original feature vector. Second, each subset is associated with pK-NN expert and the final classification decision is based on combining results produced by all experts.
Citation:
Aleksey Fadeev, Oualid Missaoui, Hichem Frigui, "Ensemble Possibilistic K-NN for Functional Clustering of Gene Expression Profiles in Human Cancers Challenge," icmla, pp.439-442, 2009 International Conference on Machine Learning and Applications, 2009
Usage of this product signifies your acceptance of the Terms of Use.