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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: 2007 IEEE Congress on Evolutionary Computation. IEEE, pp. 3759-3765. ISBN 978-1-4244-1339-3. (doi:10.1109/CEC.2007.4424960) (KAR id:14546)

Language: English
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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: Book section
DOI/Identification number: 10.1109/CEC.2007.4424960
Uncontrolled keywords: training data; data mining; benchmark testing; cloning; machine learning algorithms; classification algorithms; Euclidean distance; genetic mutations; size control; resource management
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:04 UTC
Last Modified: 16 Nov 2021 09:52 UTC
Resource URI: (The current URI for this page, for reference purposes)
Freitas, Alex A.:
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