Abstract
One Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines for outlier or novelty detection. But, traditional methods based one class classifier is very time consuming due to its iterative process for various parameters tuning. This paper presents four novel different OCC methods with their ten variants based on extreme Learning Machine (ELM). As we know, threshold decision is a crucial factor in case of OCC, so, three different threshold declining criteria have been employed so far. Our proposed classifiers mainly lie in two categories i.e. out of four proposed one class classifiers, two classifiers belong to reconstruction based and two belong to boundary based. In four proposed methods, two methods perform random feature mapping and two methods perform kernel feature mapping. These methods are tested on three benchmark datasets and exhibit better performance compared to eleven traditional one class classifiers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Pimentel, M.A.F., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. In: Signal Processing, vol. 99, pp. 215–249. Elsevier (2014)
Tax, D.L.: One class classification: concept-learning in the absence of counter-examples. PhD thesis, Delft University of Technology (2001)
Moya, M., Koch, M., Hostetler, L.: One-class classifier networks for target recognition applications. In: Proceedings of the World Congress on Neural Networks, International Neural Network Society, pp. 797–801 (1993)
Japkowicz, N.: Concept-learning in the absence of counterexamples: an autoassociation-based approach to classification. PhD thesis, New Brunswick Rutgers, The State University of New Jersey (1999)
Tax, D., Duin, R.: Support vector data description. Mach. Learn. 54, 45–66 (2004)
Lee, W., Liu, B.: Learning with positive and unlabeled examples using weighted logistic regression. In: Proceedings of the 20th International Conference on Machine Learning (ICML) (2003)
Leng, Q., Qi, H., Miao, J., Zhu, W., Su, G.: One-Class Classification with Extreme Learning Machine, Mathematical Problems in Engineering. Hindawi Publishing Corporation, Article ID: 412957 (2014)
Wang, Y., Li, D., Du, Y., Pan, Z.: Anomaly detection in traffic using L1-norm minimization extreme learning machine. Neurocomputing 149, 415–425 (2015)
Farias, G., Oliveira, A., Cabral, G.: Extreme learning machines for intrusion detection systems, neural information processing. In: Proceedings of the 19th International Conference, ICONIP 2012, Doha, Qatar, pp. 535–543 (2012)
Xiang, J., Westerlund, M., Sovilj, D., Pulkkis, G.: Using extreme learning machine for intrusion detection in a big data environment. In: Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop (AISec’14), pp. 73–82. ACM, New York, USA (2014)
Huang, G.B., Zhu, Q., Siew, C.: Extreme learning machine: a new learning scheme of feedforward neural networks. Int. Joint Conf. Neural Netw. 2, 985–990 (2004)
Huang, G.B., Zhu, Q.C., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing, vol. 70, pp. 489–501. Elsevier (2006)
Huang, G.B., Zhou, H., Ding, X., Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 42, 513–529 (2012)
Lichman, M.: UCI machine learning repository [http://archive.ics.uci.edu/ml]. University of California, School of Information and Computer Science, Irvine, CA (2013)
http://homepage.tudelft.nl/n9d04/occ/index.html. Accessed 14 Sept 2015
Mangasarian, O.L., Setiono, R., Wolberg, W.H.: Pattern recognition via linear programming: theory and application to medical diagnosis. In: Large-scale numerical optimization, pp. 22–30. SIAM Publications, Philadelphia (1990)
Tax, D.M.J.: DD tools 2014, the data description toolbox for MATLAB, version 2.1.1 [http://prlab.tudelft.nl/david-tax/dd_tools.html] (2014)
Tax, D.M.J., Muller, K.R.: A consistency-based model selection for one-class classification. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR’04), IEEE Computer Society, Los Alamitos, Calif, USA, pp. 363–366 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Gautam, C., Tiwari, A. (2016). On the Construction of Extreme Learning Machine for One Class Classifier. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 1. Proceedings in Adaptation, Learning and Optimization, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-28397-5_35
Download citation
DOI: https://doi.org/10.1007/978-3-319-28397-5_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-28396-8
Online ISBN: 978-3-319-28397-5
eBook Packages: EngineeringEngineering (R0)