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Prototype reduction using an artificial immune model

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Abstract

Artificial immune system (AIS)-based pattern classification approach is relatively new in the field of pattern recognition. The study explores the potentiality of this paradigm in the context of prototype selection task that is primarily effective in improving the classification performance of nearest-neighbor (NN) classifier and also partially in reducing its storage and computing time requirement. The clonal selection model of immunology has been incorporated to condense the original prototype set, and performance is verified by employing the proposed technique in a practical optical character recognition (OCR) system as well as for training and testing of a set of benchmark databases available in the public domain. The effect of control parameters is analyzed and the efficiency of the method is compared with another existing techniques often used for prototype selection. In the case of the OCR system, empirical study shows that the proposed approach exhibits very good generalization ability in generating a smaller prototype library from a larger one and at the same time giving a substantial improvement in the classification accuracy of the underlying NN classifier. The improvement in performance has been statistically verified. Consideration of both OCR data and public domain datasets demonstrate that the proposed method gives results better than or at least comparable to that of some existing techniques.

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Notes

  1. Instead of Hamming distance, the present experiment also considers the use of Euclidean distance in measuring stimulation value. In this case, 448-dimensional features need not be converted into binary. Since the minimum and maximum values that can occur in each dimension are known, distance between a pair of patterns is normalized to give a stimulation measure in [0, 1]. However, by using Euclidean distance instead of Hamming distance no significant change was observed in the experimental results. All the results presented here are obtained when Hamming distance was used to measure stimulation.

  2. No iteration is needed if an antigen finds an exact match in the memory. In such a case, producing clones won’t help to find any better B cell and that is why hyper-mutation phase is not invoked at all.

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  1. Computer Vision and Pattern Recognition Unit, Indian Statistical Institute, 203, B.T. Road, Kolkata, India

    Utpal Garain

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  1. Utpal Garain
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Garain, U. Prototype reduction using an artificial immune model. Pattern Anal Applic 11, 353–363 (2008). https://doi.org/10.1007/s10044-008-0106-1

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