Article ID Journal Published Year Pages File Type
496260 Applied Soft Computing 2008 13 Pages PDF
Abstract

In the paper a new classification method is proposed. It is based on Negative Selection, which was originally designed for anomaly detection and dichotomic classification. In our earlier work we described M-NSA algorithm that can be applied in multi-class classification problems. Trying to improve classification accuracy of M-NSA we propose a new version of this algorithm, called MINSA, where refinement of receptors set is applied. The accuracy of MINSA was tested in an experimental way with the use of benchmark data sets. The experiments confirmed that direction of changes introduced in MINSA improves its accuracy in comparison to M-NSA. Comparison with other methods of classification is also shown in the paper.

Related Topics
Physical Sciences and Engineering Computer Science Computer Science Applications
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