Artificial Immune Systems (AIS) are class of computational intelligent methods developed based on the principles and processes of the biological immune system. AIS methods are categorized mainly into four types according to the inspired principles and processes of immune system. These categories are clonal selection, negative selection, immune network and danger theory. The approach of negative selection algorithm (NSA) is one of the major AIS models. NSA is a supervised learning algorithm based on the imitation of the T cells maturation process in thymus. In this imitation, detectors are used to mimic the cells, and the process of T cells maturation is simulated to generate detectors. Then, NSA classifies the specified data either as normal (self) data or as anomalous (non-self) data. In this classification task, NSA methods can make two kinds of classification errors: a self data is classified as anomalous, and a non-self data is classified as normal data.
In this thesis, a novel negative selection method, variable shaped detector (V-shaped detector), is proposed to increase the classification accuracy, or in other words decreasing classification errors. In V-shaped detector, new approaches are introduced to define self and represent detectors. V-shaped detector uses the combination of Local Outlier Factor (LOF) and kth nearest neighbor (k-NN) to determine a different radius for each self sample, thus it becomes possible to model the self space using self samples and their radii. Besides, the cubic b-spline is proposed to generate a variable shaped detector. In detector representation, the application of cubic spline is meaningful, when the edge points are used. Hence, Edge Detection (ED) algorithm is developed to find the edge points of the given self samples. V-shaped detector was tested using different data sets and compared with the well-known one-class classification method, SVM, and the similar popular negative selection method, NSA with variable-sized detector termed V-detector. The experiments show that the proposed method generates reasonable and comparable results.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12615629/index.pdf |
| Date | 01 February 2013 |
| Creators | Ataser, Zafer |
| Contributors | Alpaslan, Ferda Nur |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | Access forbidden for 1 year |
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