Abstract
‘Curse of Dimensionality’ problem in shape-space representation which is used in many network-based Artificial Immune Systems (AISs) affects classification performance at a high degree. In this paper, to increase classification accuracy, it is aimed to minimize the effect of this problem by developing an Attribute Weighted Artificial Immune System (AWAIS). To evaluate the performance of proposed system, aiNet, an algorithm that have a considerably important place among network-based AIS algorithms, was used for comparison with our developed algorithm. Two artificial data sets used in aiNet, Two-spirals data set and Chainlin k data set were applied in the performance analyses, which led the results of classification performance by means of represented network units to be higher than aiNet. Furthermore, to evaluate performance of the algorithm in a real world application, wine data set that taken from UCI Machine Learning Repository is used. For the artificial data sets, proposed system reached 100% classification accuracy with only a few numbers of network units and for the real world data set, wine data set, the algorithm obtained 98.23% classification accuracy which is very satisfying result if it is considered that the maximum classification accuracy obtained with other systems is 98.9%.
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Şahan, S., Kodaz, H., Güneş, S., Polat, K. (2004). A New Classifier Based on Attribute Weighted Artificial Immune System (AWAIS). In: Aykanat, C., Dayar, T., Körpeoğlu, İ. (eds) Computer and Information Sciences - ISCIS 2004. ISCIS 2004. Lecture Notes in Computer Science, vol 3280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30182-0_2
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DOI: https://doi.org/10.1007/978-3-540-30182-0_2
Publisher Name: Springer, Berlin, Heidelberg
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