A decision hyper plane heuristic based artificial immune network classification algorithm

Abstract

Most of the developed immune based classifiers generate antibodies randomly, which has negative effect on the classification performance. In order to guide the antibody generation effectively, a decision hyper plane heuristic based artificial immune network classification algorithm (DHPAINC) is proposed. DHPAINC taboos the inner regions of the class domain, thus, the antibody generation is limited near the class domain boundary. Then, the antibodies are evaluated by their recognition abilities, and the antibodies of low recognition abilities are removed to avoid over-fitting. Finally, the high quality antibodies tend to be stable in the immune network. The algorithm was applied to two simulated datasets classification, and the results show that the decision hyper planes determined by the antibodies fit the class domain boundaries well. Moreover, the algorithm was applied to UCI datasets classification and emotional speech recognition, and the results show that the algorithm has good performance, which means that DHPAINC is a promising classifier.

Keywords

artificial immune network / decision hyper plane / recognition ability / classification

References

[1] JonathanT, AndrewH, ThomasS, EdwardC. Theoretical advances in artificial immune systems [J]. Theoretical Computer Science, 2008, 403(1): 11-32

[2] LiZ-h, ZhangY-n, TanH-zhou. IA-AIS: An improved adaptive artificial immune system applied to complex optimization problems [J]. Applied Soft Computing, 2011, 11(8): 4692-4700

[3] ZhongY-f, ZhangL-p, LiP-xiang. Classification of multi-spectral remote sensing image based on multiple-valued immune network [J]. Chinese Journal of Computers, 2007, 30(12): 2181-2188

[4] DengZ-l, TanG-z, YeJ-x, FanB-shuang. An immune classification algorithm for breast cancer diagnosis [J]. Journal of Central South University: Science and Technology, 2010, 41(4): 1485-1490

[5] YangD-d, JiaoL-c, GongM-g, LiuFang. Artificial immune multi-objective SAR image segmentation with fused complementary features [J]. Information Sciences, 2011, 181(13): 2797-2812

[6] OuC-ming. Host-based intrusion detection systems adapted from agent-based artificial immune systems [J]. Neurocomputing, 2012, 88(1): 78-86

[7] AlexanderO T. Immunocomputing for intelligent intrusion detection [J]. IEEE Computational Intelligence Magazine, 2008, 3(2): 22-30

[8] AndrewB WAIRS: A resource limited artificial immune classifier [D], 2001Mississippi StateDepartment of Computer Science and Engineering, Mississippi State University

[9] KevinL, FranceC, ChristopherC. Generating compact classifier systems using a simple artificial immune system [J]. IEEE Trans Syst Man Cybernet-Part B, 2007, 37(5): 1344-1356

[10] IlhanA, MehmetK, ErhanA. Artificial immune classifier with swarm learning [J]. Engineering Applications of Artificial Intelligence, 2010, 23(8): 1291-1302

[11] LiuR-c, NiuM-c, JiaoL-cheng. A new artificial immune network algorithm for classifying complex data [J]. Journal of Electronics & Information Technology, 2010, 32(3): 515-521

[12] KazushiI, HirotadaO. A negative selection algorithm for classification and reduction of the noise effect [J]. Applied Soft Computing, 2009, 9(1): 431-438

[13] JasonBThe clonal selection classification algorithm (CSCA) [R], 2005MelbourneSwinburne University of Technology

[14] UsamaM F, KekiB INebelB. Multi-Interval discretization for continuous-valued attributes for classification learning [C]. hirteenth International Joint Conference on Artificial Intelligence. France, Morgan Kaufmann, 19931022-1027

[15] SuryannarayanaC, AmitavaC, SugataM. Support vector machines employing cross-correlation for emotional speech recognition [J]. Measurement, 2009, 42(1): 611-618