Abstract
We focus on developing a pattern recognition method suitable for performing supervised analysis tasks on molecular data resulting from microarray experiments. Molecular characterization of tissue samples using microarray gene expression profiling is expected to uncover fundamental aspects related to cancer diagnosis and drug discovery. There is therefore a need for reliable, accurate classification methods. With this study we propose a framework for constructing an ensemble of individually trained SVM classifiers, each of them specialized on subsets of the input space. The fuzzy approach used for partitioning the data produces overlapping subsets of the input space that facilitates subsequent classification tasks.
Original language | English (US) |
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Pages (from-to) | 5834-5837 |
Number of pages | 4 |
Journal | Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference |
State | Published - 2006 |
Externally published | Yes |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Signal Processing
- Biomedical Engineering
- Health Informatics