Abstract
Support vector data description (SVDD), an efficient one-class classification method, captures the spherically shaped boundary around the same class data and achieves classification for setting the boundary related to support vectors (SVs). As SVDD constructs an irregular hypersphere in high-dimensional space, it is unreasonable to keep the classification boundary a constant value. When the classification dataset is complicated, constant classification boundary will decrease the accuracy of classification. In this paper, we present a dynamic hypersphere SVDD (DH-SVDD) without describing boundary for one-class classification. In training process, important SVs of training dataset describe the static hypersphere. In testing process, dynamic hypersphere is described according to the new important SVs of the testing sample and training dataset. If there is a significant change of hypersphere structure, it means the new sample is an outlier. In this method, without any classification boundary, it can complete one-class classification with fully considering the related information of new sample and historical dataset. Thus, it can significantly improve the one-class classification accuracy of SVDD in complex datasets. Comparison is conducted among the proposed DH-SVDD, K-chart SVDD, Max limit SVDD and Validation limit SVDD. The effectiveness of the proposed method is also verified by the experimental UCI datasets.
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References
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Tax DMJ, Duin RPW (1999) Support vector domain description. Pattern Recogn Lett 20(11):1191–1199
Tax DMJ, Duin RPW (2004) Support vector data description. Mach Learn 54(1):45–66
Kourti T (2003) Multivariate dynamic data modeling for analysis and statistical process control of batch processes, start-ups and grade transitions. J Chemom 17(1):93–109
Ge Z, Song Z (2014) Online monitoring and quality prediction of multiphase batch processes with uneven length problem. Ind Eng Chem Res 53(2):800–811
Shin JH, Lee B, Park KS (2011) Detection of abnormal living patterns for elderly living alone using support vector data description. IEEE T Inf Technol B 15(3):438–448
Wang S, Yu J, Lapira E, Lee J (2013) A modified support vector data description based novelty detection approach for machinery components. Appl Soft Comput 13(2):1193–1205
Prakash J, Singh PK (2015) An effective multi objective approach for hard partitional clustering. Memet Comput 7(2):93–104
Jiang Q, Yan X, Lv Z, Guo M (2014) Independent component analysis-based non-Gaussian process monitoring with preselecting optimal components and support vector data description. Int J Prod Res 52(11):3273–3286
Ge Z, Xie L, Kruger U, Lamout L, Song Z (2009) Sensor fault identification and isolation for multivariate non-Gaussian processes. J Process Control 19(10):1707–1715
Chen YC, Su CT (2016) Distance-based margin support vector machine for classification. Appl Math Comput 283:141–152
Lv Z, Yan X (2016) Hierarchical support vector data description for batch process monitoring. Ind Eng Chem Res 55(34):9205–9214
Sun R, Tsung F (2003) A kernel-distance-based multivariate control chart using support vector methods. Int J Prod Res 41(13):2975–2989
Kittiwachana S, Ferreira DLS, Lloyd GR, Fido LA, Thompson DR, Escott RE, Brereton RG (2010) One class classifiers for process monitoring illustrated by the application to online HPLC of a continuous process. J Chemom 24(3–4):96–110
Jiang Q, Yan X (2014) Just-in-time reorganized PCA integrated with SVDD for chemical process monitoring. AIChE J 60(3):949–965
Khediri IB, Weihs C, Limam M (2012) Kernel k-means clustering based local support vector domain description fault detection of multimodal processes. Expert Syst Appl 39(2):2166–2171
Kumar S, Choudhary AK, Kumar M, Shankar R, Tiwari MK (2006) Kernel distance-based robust support vector methods and its application in developing a robust K-chart. Int J Prod Res 44(1):77–96
Ning X, Tsung F (2013) Improved design of kernel distance-based charts using support vector methods. IIE Trans 45(4):464–476
Ge Z, Song Z (2013) Bagging support vector data description model for batch process monitoring. J Process Control 23(8):1090–1096
Mahadevan S, Shah SL (2009) Fault detection and diagnosis in process data using one-class support vector machines. J Process Control 19(10):1627–1639
Sukchotrat T, Kim SB, Tsung F (2009) One-class classification-based control charts for multivariate process monitoring. IIE Trans 42(2):107–120
Yao M, Wang H, Xu W (2014) Batch process monitoring based on functional data analysis and support vector data description. J Process Control 24(7):1085–1097
Brereton R (2009) Chemometrics for pattern recognition. Wiley, New York
Phaladiganon P, Kim SB, Chen VC (2014) A density-focused support vector data description method. Qual Reliab Eng Int 30(6):879–890
Tax DMJ (2015a) Data sets for one-class classification. http://homepage.tudelft.nl/n9d04/occ/. Accessed August 2016
Acknowledgements
This work was mainly supported by National Natural Science Foundation of China (61240047) and Natural Science Foundation of Beijing (4152041). The authors would like to thank the anonymous reviewers for their valuable comments to improve this manuscript.
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Wang, J., Liu, W., Qiu, K. et al. Dynamic hypersphere SVDD without describing boundary for one-class classification. Neural Comput & Applic 31, 3295–3305 (2019). https://doi.org/10.1007/s00521-017-3277-0
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DOI: https://doi.org/10.1007/s00521-017-3277-0