Abstract
Artificial immune system (AIS) algorithms have been successfully applied in the domain of supervised learning. The main objective of supervised learning algorithms is to generate a robust and generalized model that can work well not only on seen data (training data) but also predict well on unseen data (test data). One of the main issues with supervised learning approaches is model overfitting. Model overfitting occurs when there is insufficient training data, or training data is too simple to cover the structural complexity of the domain being modelled. In overfitting, the final model works well on training data because the model is specialized on training data but provides significantly inaccurate predictions on test data due to the model’s lack of generalization capabilities. In this paper, we propose a novel approach to address this model overfitting that is inspired by the processes of natural immune systems. Here, we propose that the issue of overfitting can be addressed by generating more data samples by analyzing existing scarce data. The proposed approach is tested on benchmarked datasets using two different classifiers, namely, artificial neural networks and C4.5 (decision tree algorithm).
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Kotsiantis, S.B.: Supervised machine learning: A review of classification techniques. Informatica (Ljubljana) 31, 249–268 (2007)
Burges, C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Murthy, S.K.: Automatic construction of decision trees from data: A multi-disciplinary survey. Data Mining and Knowledge Discovery 2(4), 345–389 (1998)
Moore, A.W.: Cross-validation for detecting and preventing overfitting. School of Computer Science Carneigie Mellon University (2001)
Larose, D.T.: k‐Nearest Neighbor Algorithm. Discovering Knowledge in Data: An Introduction to Data Mining, 90–106 (2005)
Watkins, A., Timmis, J., Boggess, L.: Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm. Genetic Programming and Evolvable Machines 5(3), 291–317 (2004)
Ahmad, W., Narayanan, A.: Outlier detection using humoral-mediated clustering (HAIS). In: Proceedings of IEEE World Congress on Nature and Biologically Inspired Computing, NaBIC2010, pp. 45–52 (2010)
Castro, L.N.D., Zuben, J.: The clonal selection algorithm with engineering applications. In: Workshop Proceedings of GECCO, Workshop on Artificial Immune Systems and Their Applications, Las Vegas, pp. 36–37 (2000)
Li, X., et al.: ICAIS: a novel incremental clustering algorithm based on artificial immune system. In: International Conference on Internet Computing in Science and Engineering, pp. 85–90 (2008)
Liu, Z., et al.: FAISC: a fuzzy artificial immune system clustering algorithm. In: Third International Conference on Natural Computation (ICNC 2007) (2007)
Yap, F.W., Koh, S.P., Tiong, S.K.: Mathematical Function Optimization using AIS Antibody Remainder method. International Journal of Machine Learning and Computing 1(1), 13–19 (2011)
Ahmad, W., Narayanan, A.: Principles and methods of artificial immune system vaccination of learning systems. In: Liò, P., Nicosia, G., Stibor, T. (eds.) ICARIS 2011. LNCS, vol. 6825, pp. 268–281. Springer, Heidelberg (2011)
Castro, L.N.D., Zuben, F.J.V.: aiNet: an artificial immune network for data analysis. In: Abbass, H., Sarker, R., Newton, C. (eds.) Data Mining: A Heuristic Approach, ch012, pp. 231–260. Idea Group Publishing (2002). doi:10.4018/978-1-930708-25-9
Younsi, R., Wang, W.: A new artificial immune system algorithm for clustering. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 58–64. Springer, Heidelberg (2004)
Brownlee, J.: Clonal Selection Theory and CLONALG: The Clonal Selection Classification Algorithm (CSCA). Technical Report 2-02, CISCP, Swinburne University of Technology (2005)
Khaled, A., Abdul-Kader, H.M., Ismail, N.A.: Artificial Immune Clonal Selection Algorithm: A Comparative Study of CLONALG, opt-IA and BCA with Numerical Optimization Problems. International Journal of Computer Science and Network Security 10(4), 24–30 (2010)
Ahmad, W., Narayanan, A.: Humoral artificial immune system (HAIS) for supervised learning. In: Proceedings of IEEE World Congress on Nature and Biologically Inspired Computing, NaBIC2010, pp. 37–44 (2010)
Woldemariam, K.M., Yen, G.G.: Vaccine-Enhanced Artificial Immune System for Multimodal Function Optimization. IEEE Transactions on Systems, Man and Cybernetics 40(1), 218–228 (2010)
Castro, P.A.D., Von Zuben, F.J.: MOBAIS: a bayesian artificial immune system for multi-objective optimization. In: Bentley, P.J., Lee, D., Jung, S. (eds.) ICARIS 2008. LNCS, vol. 5132, pp. 48–59. Springer, Heidelberg (2008)
Castro, L.N.D., Timmis, J.: An artificial immune network for multimodal function optimisation. In: Proceedings of IEEE World Congress on Evolutionary Computation, pp. 669–674 (2002)
Kelsey, J., Timmis, J.: Immune inspired somatic contiguous hypermutation for function optimisation. In: Cantú-Paz, E., et al. (eds.) GECCO 2003. LNCS, vol. 2723, pp. 207–218. Springer, Heidelberg (2003)
WEKA, Machine Learning Group at the University of Waikato. Weka 3: Data Mining Software in Java
Zhang, G.: Neural networks for classification: a survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C 30(4), 451–462 (2000)
Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial Neural Networks: A Tutorial. IEEE Computer, 31–44 (1996)
Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)
Quinlin, J.R.: C4. 5: programs for machine learning. Morgan Kaufmann (1993)
UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Ahmad, W., Narayanan, A. (2015). Artificial Immune System: An Effective Way to Reduce Model Overfitting. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9329. Springer, Cham. https://doi.org/10.1007/978-3-319-24069-5_30
Download citation
DOI: https://doi.org/10.1007/978-3-319-24069-5_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24068-8
Online ISBN: 978-3-319-24069-5
eBook Packages: Computer ScienceComputer Science (R0)