Memory Cell Pruning and Nonlinear Resource Allocation BasedArtificial Immune Recognition System
DENG Ze-Lin1,2 ,TAN Guan-Zheng1,HE Pei2
1. School of Information Science and Engineering,Central South University,Changsha 410083 2.School of Computer and Communication Engineering,Changsha University of Science and Technology,Changsha 410076
Abstract：To reduce the memory cells of artificial immune recognition system (AIRS) and improve AIRS classification performance,a memory cell pruning and nonlinear resource allocation based artificial immune recognition system (PNAIRS) is proposed. Attribute discretization pre-processing is adopted to compress the training space. Memory cell pruning operation is employed to eliminate the memory cells of low fitness scores,and nonlinear resource allocation is utilized to optimize the classifier. PNAIRS is applied to 6 UCI datasets classification,the classification performance is compared with other classifiers. PNAIRS generates small memory cell population and reaches high classification accuracy,and the classification is finished quickly. The results show that PNAIRS is a high-performance classifier,and it has potential application.
[1\]Timmis J,Honec A,Stibord T,et al. Theoretical Advances in Artificial Immune Systems. Theoretical Computer Science,2008,403 (1): 11-32 [2]Qi Yutao,Liu Fan,Jiao Licheng. Immune Algorithm with Self Adaptive Reduction for Large Scale TSP. Journal of Software,2008,19 (6): 1265-1273(in Chinese) (戚玉涛,刘 芳,焦李成.求解大规模TSP问题的自适应归约免疫算法.软件学报,2008,19(6): 1265-1273) [3]Zhao Yunfeng,Fu Dongmei,Yin Yixin,et al. Application of Improved Artificial Immune Algorithm in Parameter Optimization for Image Registration. High Technology Letters,2009,19(5): 525-532 (in Chinese) (赵云丰,付冬梅,尹怡欣,等.改进的人工免疫算法在图像配准参数优化中的应用.高技术通讯,2009,19(5): 525-532) [4]Zhong Yanfei,Zhang Liangpei,Li Pingxiang. Classification of Multi Spectral Remote Sensing Image Based on Multiple Valued Immune Network. Chinese Journal of Computers,2007,30(12): 2181-2188(in Chinese) (钟燕飞,张良培,李平湘.基于多值免疫网络的多光谱遥感影像分类.计算机学报,2007,30(12): 2181-2188) [5]Timmis J,Neal M. A Resource Limited Artificial Immune System 2for Data Analysis. Knowledge Based Systems,2001,14(3/4): 121-130 [6]Yang Dongdong,Jiao Licheng,Gong Maoguo,et al. Artificial Immune Multi Objective SAR Image Segmentation with Fused Complementary Features. Information Sciences,2011,181(13): 2797-2812 [7]Liu Fan,Luo Lan. Adaptive Immune Wavelet Network Based Intrusion Detection. Pattern Recognition and Artificial Intelligence,2006,19(2): 243-248(in Chinese) (刘 芳,骆 岚.基于免疫自适应小波网络的入侵验证.模式识别与人工智能,2006,19(2): 243-248) [8]Tarakanov A O. Immunocomputing for Intelligent Intrusion Detection. IEEE Computational Intelligence Magazine,2008,3(2): 22-30 [9]Gong Maoguo,Jiao Licheng,Ma Wenping,et al. Unsupervised Classification and Recognition Using an Artificial Immune System Based on Manifold Distance. Acta Automatica Sinica,2008,34(3): 367-375(in Chinese) (公茂果,焦李成,马文萍,等.基于流形距离的人工免疫无监督分类与识别算法.自动化学报,2008,34(3): 367-375) [10]Zhang Chenggong,Zhang Yi. Tree Structured Artificial Immune Network with Self Organizing Reaction Operator. Neurocomputing,2009,73 (1/2/3): 336-349 [11]Xiong Hao,Sun Caixin. Artificial Immune Network Classification Algorithm for Fault Diagnosis of Power Transformer. IEEE Trans on Power Delivery,2007,22(2): 930-935 [12]Deng Zelin,Tan Guanzheng,Fan Bishuan,et al. Research Progress on Immune Classification. Computer Engineering and Applications,2011,47(16): 8-11(in Chinese) (邓泽林,谭冠政,范必双,等. 免疫分类研究进展.计算机工程与应用,2011,47(16): 8-11) [13]Watkins A. AIRS: A Resource Limited Artificial Immune Classifier. Master Dissertation. Mississippi,USA: Mississippi State University,2001 [14]Leung K,Cheong F,Cheong C. Generating Compact Classifier Systems Using a Simple Artificial Immune System. IEEE Trans on Systems,Man and Cybernetics,2007,37(5): 1344-1356 [15]Aydin I,Mehmet K,Erhan A. Artificial Immune Classifier with Swarm Learning. Engineering Applications of Artificial Intelligence,2010,23(8): 1291-1302 [16]Brownlee J. The Clonal Selection Classification Algorithm. Technical Report,2-02. Melbourne,Australia: Swinburne University of Technology,2005 [17]Liu Ruochen,Niu Manchun,Jiao Licheng. A New Artificial Immune Network Algorithm for Classifying Complex Data. Journal of Electronics & Information Technology,2010,32(3): 515-521(in Chinese) (刘若辰,钮满春,焦李成.一种新的人工免疫网络算法及其在复杂数据分类中的应用.电子与信息学报,2010,32(3): 515-521) [18]Igawa K,Ohashi H. A Negative Selection Algorithm for Classification and Reduction of the Noise Effect. Applied Soft Computing,2009,9(1): 431-438 [19]Fayyad U M,Irani K B. Multi Interval Discretization for Continuous Valued Attributes for Classification Learning // Proc of the 13th International Joint Conference on Artificial Intelligence. Beijing,China,1993,II: 1022-1027 [20]Ozsen S,Gunes S,Kara S,et al. Use of Kernel Functions in Artificial Immune Systems for the Nonlinear Classification Problems. IEEE Trans on Information Technology in Biomedicine,2009,13(4): 621-628 [21]Polat K,Günus S. A New Feature Selection Method on Classification of Medical Datasets: Kernel F Score Feature Selection. Expert Systems with Applications,2009,36(7): 10367-10373 [22]Polat K,Günus S,Arslan A. A Cascade Learning System for Classification of Diabetes Disease: Generalized Discriminant Analysis and Least Square Support Vector Machine. Expert Systems with Applications,2008,34(1): 482-487
2013, Vol. 26 
