WAIRS: Improving Classification Accuracy by Weighting Attributes in the AIRS Classifier

Secker, Andrew D. and Freitas, Alex A. (2007) WAIRS: Improving Classification Accuracy by Weighting Attributes in the AIRS Classifier. In: Evolutionary Computation, 2007. CEC 2007. IEEE Congress on. IEEE Congress on Evolutionary Computation . IEEE Press, Singapore pp. 3759-3765. ISBN 978-1-4244-1339-3. (doi:https://doi.org/10.1109/CEC.2007.4424960) (Full text available)

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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.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:04 UTC
Last Modified: 30 Jun 2017 11:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14546 (The current URI for this page, for reference purposes)
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