Ozsen, SeralYucelbas, Cuneyt2020-03-262020-03-2620151568-49461872-9681https://dx.doi.org/10.1016/j.asoc.2015.03.014https://hdl.handle.net/20.500.12395/32388Using different shapes of recognition regions in Artificial Immune Systems (AIS) are not a new issue. Especially, ellipsoidal shapes seem to be more intriguing as they have also been used very effectively in other shape space-based classification methods. Some studies have done in AIS through generating ellipsoidal detectors but they are restricted in their detector generating scheme - Genetic Algorithms (GA). In this study, an AIS was developed with ellipsoidal recognition regions by inspiring from the clonal selection principle and an effective search procedure for ellipsoidal regions was applied. Performance evaluation tests were conducted as well as application results on some real-world classification problems taken from UCI machine learning repository were obtained. Comparison with GA was also done in some of these problems. Very effective and comparatively good classification ratios were recorded. (C) 2015 Elsevier B.V. All rights reserved.en10.1016/j.asoc.2015.03.014info:eu-repo/semantics/closedAccessClassificationArtificial Immune SystemsNonlinear classificationEllipsoidal recognition regionsClonal selection principleArticle31210222Q1WOS:000352955600016Q1