Effect of nonlinear resource allocation on AIRS classifier accuracy
Artificial Immune Recognition System (AIRS) is most popular immune inspired classifier. It also has shown itself to be a competitive classifier. AIRS uses linear method to allocate resources. In this paper, two different nonlinear resource allocation methods apply to AIRS. Then new algorithms are te...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
Universiti Utara Malaysia
2008
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| Online Access: | http://psasir.upm.edu.my/id/eprint/59741/ http://psasir.upm.edu.my/id/eprint/59741/1/596-600-CR162.pdf |
| Summary: | Artificial Immune Recognition System (AIRS) is most popular immune inspired classifier. It also has shown itself to be a competitive classifier. AIRS uses linear method to allocate resources. In this paper, two different nonlinear resource allocation methods apply to AIRS. Then new algorithms are tested on 8 benchmark datasets. Based on the results of experiments, one of them increases the accuracy of AIRS in the majority of cases. |
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