Abstract
One-class classification/data description plays a key roles in numerous applications such as anomaly detection. This paper presents a novel ensemble one-class extreme learning machine (EOCELM), which not only yields sound performance but also facilitates the parallel processing of training and testing. Instead of training on the entire training dataset, EOCELM first partitions the training data into overlapping clusters by k-medoids clustering and a simple Minimum Spanning Tree (MST) based heuristic rule. The proposed overlapping data partition makes it possible to describe the sub-structures within one-class training data more precisely without the risk of creating “clutser gap” that may degrade the generalization performance. Besides, the data partition can alleviate the matrix inversion problem of original extreme learning machine (OCELM) when dealing with massive training data. Next, an OCELM is trained for each data cluster as a sub-classifier, which can be implemented in a parallel way. Finally, OCELMs are combined into EOCELM by the simple maximum combining rule. Experiments on synthetic datasets, UCI datasets and MNIST datasets demonstrate the effectiveness of EOCELM when compared with other state-of-the-art one-class learning approaches.
S. Wang—This work was supported by the National Natural Science Foundation of China (Project No. 60970034, 61170287, 61232016).
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References
Tax, D.M.J.: One-class classification. Ph.D. thesis (2001)
Manevitz, L.M., Yousef, M.: One-class SVMs for document classification. J. Mach. Learn. Res. 2(1), 139–154 (2001)
Erfani, S.M., Rajasegarar, S., Karunasekera, S., Leckie, C.: High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recogn. 58, 121–134 (2016)
Shin, H.J., Eom, D.H., Kim, S.S.: One-class support vector machines an application in machine fault detection and classification. Comput. Ind. Eng. 48(2), 395–408 (2005)
Zuo, H., Wu, O., Hu, W., Xu, B.: Recognition of blue movies by fusion of audio and video. In: IEEE International Conference on Multimedia and Expo, pp. 37–40 (2008)
Cohen, G., Sax, H., Geissbuhler, A.: Novelty detection using one-class parzen density estimator. An application to surveillance of nosocomial infections. Stud. Health Technol. Inform. 136, 21–26 (2008)
Dasarathy, B.V.: Adaptive local fusion systems for novelty detection and diagnostics in condition monitoring. Proc. SPIE Int. Soc. Opt. Eng. 3376, 210–218 (1998)
Manevitz, L., Yousef, M.: One-class document classification via neural networks. Neurocomputing 70(7–9), 1466–1481 (2007)
Schlkopf, B., Platt, J.C., Shawetaylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)
Leng, Q., Qi, H., Miao, J., Zhu, W., Su, G.: One-class classification with extreme learning machine. Math. Probl. Eng. 2015, 1–11 (2015)
Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)
Rabaoui, A., Davy, M., Rossignol, S., Lachiri, Z., Ellouze, N.: Improved one-class SVM classifier for sounds classification. In: IEEE Conference on Advanced Video and Signal Based Surveillance, AVSS 2007, pp. 117–122. IEEE (2007)
Xiao, Y., Wang, H., Zhang, L., Xu, W.: Two methods of selecting Gaussian kernel parameters for one-class SVM and their application to fault detection. Knowl. Based Syst. 59(2), 75–84 (2014)
Lecomte, S., Lengelle, R., Richard, C., Capman, F., Ravera, B.: Abnormal events detection using unsupervised One-Class SVM-Application to audio surveillance and evaluation. In: IEEE International Conference on Advanced Video and Signal-Based Surveillance, pp. 124–129 (2011)
Huang, G.B., Zhu, Q.Y., Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70(1–3), 489–501 (2006)
Liu, J., Miao, Q., Sun, Y., Song, J., Quan, Y.: Fast structural ensemble for one-class classification. Pattern Recogn. Lett. 80, 179–187 (2016)
Krawczyk, B., Woniak, M., Cyganek, B.: Clustering-based ensembles for one-class classification. Inf. Sci. 264(6), 182–195 (2013)
Tax, D.M.J., Duin, R.P.W.: Combining one-class classifiers. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 299–308. Springer, Heidelberg (2001). doi:10.1007/3-540-48219-9_30
Liu, J., Miao, Q., Sun, Y., Song, J., Quan, Y.: Modular ensembles for one-class classification based on density analysis. Neurocomputing 171(C), 262–276 (2016)
Dsir, C., Bernard, S., Petitjean, C., Heutte, L.: One class random forests. Pattern Recogn. 46(12), 3490–3506 (2013)
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Wang, S., Zhao, L., Zhu, E., Yin, J., Yang, H. (2017). Ensemble One-Class Extreme Learning Machine Based on Overlapping Data Partition. In: Sun, F., Liu, H., Hu, D. (eds) Cognitive Systems and Signal Processing. ICCSIP 2016. Communications in Computer and Information Science, vol 710. Springer, Singapore. https://doi.org/10.1007/978-981-10-5230-9_40
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