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A new fuzzy support vectors machine for biomedical data classification.
- Source :
-
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2008; Vol. 2008, pp. 4676-9. - Publication Year :
- 2008
-
Abstract
- In this paper a new approach to a fuzzy support vector machine (FSVM) for solving multi-class problems is presented. The developed algorithm combines two separate methods based on fuzzy support vector machine, one for solving two-class problems and the second for multi-class problems. The first method deals with the problem of selecting the best support vector machine (SVM) kernel function and the second method enables classification of unclassified regions that appear when classical SVM methods for solving multi-class problems are used. Presented tool has been subjected to the dataset from Kent Ridge Biomedical Data Set Repository and showed its superiority comparing with conventional SVM and FSVM methods.
- Subjects :
- Algorithms
Artificial Intelligence
Computer Simulation
Databases, Factual
Decision Support Techniques
Fuzzy Logic
Humans
Information Storage and Retrieval methods
Models, Statistical
Neural Networks, Computer
Oligonucleotide Array Sequence Analysis instrumentation
Reproducibility of Results
Computational Biology instrumentation
Computational Biology methods
Data Interpretation, Statistical
Oligonucleotide Array Sequence Analysis methods
Pattern Recognition, Automated methods
Subjects
Details
- Language :
- English
- ISSN :
- 2375-7477
- Volume :
- 2008
- Database :
- MEDLINE
- Journal :
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
- Publication Type :
- Academic Journal
- Accession number :
- 19163759
- Full Text :
- https://doi.org/10.1109/IEMBS.2008.4650256