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CEC
2007
IEEE

WAIRS: improving classification accuracy by weighting attributes in the AIRS classifier

14 years 5 months ago
WAIRS: improving classification accuracy by weighting attributes in the AIRS classifier
— AIRS (Artificial Immune Recognition System) has shown itself to be a competitive classifier. It has also proved to be the most popular immune inspired classifier. However, rather than AIRS being a classifier in its own right as previously described, we see AIRS more as a pre-processor to a KNN classifier. It is our view that by not explicitly classing it as such development of this algorithm has been rather held back. Seeing it as a pre-processor allows inspiration to be taken from the machine learning literature where such pre-processors are not uncommon. With this in mind, this paper takes a core feature of many such pre-processors, that of attribute weighting, and applies it to AIRS. The resultant algorithm called WAIRS (Weighted AIRS) uses a weighted distance function during all affinity evaluations. WAIRS is tested on 9 benchmark datasets and is found to outperform AIRS in the majority of cases.
Andrew Secker, Alex Alves Freitas
Added 02 Jun 2010
Updated 02 Jun 2010
Type Conference
Year 2007
Where CEC
Authors Andrew Secker, Alex Alves Freitas
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