E-CLONALG: A classifier based on Clonal Selection Algorithm

  • Rama Krushna Das National Informatics Centre Ministry of Electronics and IT Govt. of India
  • Sanghamitra Dash Department of Electronic Sciences, Berhampur University, Berhampur, Odisha, India
  • Rabindra Kishore Mishra Dpartment of Electronic Sciences, Berhampur University, Berhampur, Odisha, India
  • Arijit Panigrahy National Informatics Centre, Berhampur, Odisha, India
Keywords: Accuracy, Antibody, Artificial Immune System, classification, classifier, CLONALG, CSCA, cloning, intrusion detection, machine learning

Abstract

This paper proposes an improved version of CLONALG, Clone Selection Algorithm based on Artificial Immune System(AIS), that matches with the conventional classifiers in terms of accuracy tested on the same data sets. Clonal Selection Algorithm is an artificial immune system model. Instead of randomly selecting antibodies, it is proposed to take k memory pools consisting of all the learning cases. Also, an array averaged over the pools is created and is considered for cloning. Instead of using the best clone and calculating the similarity measure and comparing with the original cell, here, k best clones were selected, the average similarity measure was evaluated and noise was filtered. This process enhances the accuracy from 76.9 percentage to 94.2 percentage, ahead of the conventional classification methods.

References

(1) J.R. Al-Enezi, M.F. Abbod , S. Alsharhan : ‘Artificial Immune Systems- Models, Algorithms And Applications’, International Journal of Research and Reviews in Applied Sciences, 3 (2), May (2010) 118-131

(2) R. Murugesan, K. Sivasakthi Balan : ‘Positive Selection based Modified Clonal Selection Algorithm for Solving Job Shop Scheduling Problem’, Applied Mathematical Sciences, Vol. 6,2012, no. 46, 2255-2271

(3) Ramsha Rizwan, Farrukh Aslam Khan, Haider Abbas, Sajjad Hussain Chauhdary: ‘Anomaly Detection in Wireless Sensor Networks Using Immune- Based Bioinspired Mechanism’, International Journal of Distributed Sensor Networks Vol. (2015)

(4) AISWeb – The Online Home of Artficial Immune Systems (http://www.artificial-immune-systems.org/algorithms.shtml)

(5) K.Parthasarathy, ‘Clonal selection method for immunity based intrustion, detection systems’, Project Report (2014) , 1-19

(6) Junyuan Shen, Jidong Wang, Hao Ai: ‘An Improved Artificial Immune System- Based Network Intrusion Detection by Using Rough Set’, Communication and Network, 2012,4,41-47.

(7) Amira Sayed A. Aziz, mostafa A. Salama, Aboul ella Hassanien, sanna El-Ola Hanafi: ‘Artificial Immune System Inspired Instrusion detection System Using Genetic Alorithm’, Informatica 36 (2012) 347-357

(8) Julie Greensmith, Uwe Aickelin, Steve Cayzer: ‘Introducing Dendritic Cells as a Novel Immune- Inspired Algorithm for Anomaly Detection’, International Conference on Artificial Immune Systems, ICARIS (2005), 14th -17th August 2005,

Banff, Alberta, Canada.

(9) Ezgi Deniz Ulker, Sadik Ulker.: ‘Comparison Study for Clonal Selection Algorithm and Genetic Algorithm’, Int. J. of Computer. Science & Information Technology Vol 4, No.4. August (2012) 107–118

(10) Ilhan Aydin, Mehmet Karakose, Erhan Akin, ‘Generation of classification rules using artificial immune system for fault diagnosism’, IEEE Conference on Systems Man and Cybernetics (SMC), pp 343-349, (2010)

(11) Vincenzo Cutello, Giuseppe Narzisi, Giuseppe Nicosia, Mario Pavone : ‘A Comparative Case Study Using Effecctive Mutation Potentials, C. Jacob et al. (Eds.)’: ICARIS (2005), LNCS 3627, pp. 13-28,200525: 1967-1978

(12) Jason Brownlee, ‘Clonal Seleciton theory and ClonalG:The Clonal Selection Classification Algorithm’, Technical Report No, 2-02, January (2005)

(13) Linquan Xie, Ying Wang, Liping Chen, Guangxue Yue: ‘An Anomaly Detection Method Based on Fuzzy C-means Clustering Algorithm’, Second International Symposium on Networking and Network Security (ISNNS,10) Jinggangshan, P.R. China, 2-4, 2010, pp.089-092

(14) Ryma Daoudi, Khalifa Djemal, Abdelkader Benyettou: ‘Cells Clonal Selection for Breast Cancer Classification’, International MultiConference on Systems, Signals & Devices (SSD0, Hammamet, Tunisia, Mach 18-21,2013

(15) Anurag Sharma, D. Sharma, ‘Clonal Selection Algorithm for

Classification', Lecture Notes in Computer Science, Vol 6825, pp 361-370 (2011)

(16) UCI Machine Learning Repository –Adult Data Set (https://archive.ics.uci.edu/ml/datasets/Adult)

(17) Weka: Data Mining Software in Java, (http://www.cs.waikato.ac.nz/~ml/weka/)

(18) Github, (https://github.com/arijitiit)

(19) Wikipedia Kappa stat (https://en.wikipedia.org/wiki/Cohen%27s_kappa)

(20) Wikipedia RMSE stat (https://en.wikipedia.org/wiki/Root-meansquare_deviation)

Published
2017-01-07