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A Hybrid Evolutionary Approach to Obtain Better Quality Classifiers
- Source :
- Advances in Computational Intelligence ISBN: 9783642214974, IWANN (2)
- Publication Year :
- 2011
- Publisher :
- Springer Berlin Heidelberg, 2011.
-
Abstract
- We present an extra measurement for classifiers, responding to the need to evaluate them with more than accuracy alone. This measure should be able to express, at least to some degree, the extent to which all classes are taken into account in a classification problem. In this communication we propose sensitivity dispersion (being as it is, the associated statistical dispersion measurement of accuracy), as the appropriate measure to have a more complete evaluation of the quality of classifiers. We use the Evolutionary Extreme Learning Machine algorithm, with a specific fitness function to optimize both measures simultaneously, and we compare it with other classifiers.
- Subjects :
- Fitness function
Degree (graph theory)
business.industry
media_common.quotation_subject
Pattern recognition
Machine learning
computer.software_genre
Measure (mathematics)
Random subspace method
Quality (business)
Statistical dispersion
Artificial intelligence
Sensitivity (control systems)
business
computer
Mathematics
media_common
Extreme learning machine
Subjects
Details
- ISBN :
- 978-3-642-21497-4
- ISBNs :
- 9783642214974
- Database :
- OpenAIRE
- Journal :
- Advances in Computational Intelligence ISBN: 9783642214974, IWANN (2)
- Accession number :
- edsair.doi...........ebb6c41bb806d9679e7a8478867ed98d