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Soft Computing
Erschienen in: Soft Computing 11/2016

03.07.2015 | Methodologies and Application

Relative density degree induced boundary detection for one-class SVM

verfasst von: Fa Zhu, Jian Yang, Sheng Xu, Cong Gao, Ning Ye, Tongming Yin

Erschienen in: Soft Computing | Ausgabe 11/2016

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Abstract

Unlike two-class (multi-class) support vector machines, massive targets and few outliers are available in one-class support vector machine. The strategies to select useful data for two-class (multi-class) support vector machines are not suitable for one-class support vector machine. In this paper, relative density degree is introduced to select useful data for one-class support vector machine. These data would become support vectors after training and locate near the boundary of the data distribution. The relative density degree of the data near the boundary of the training set is smaller than that of the data in the interior of the training set. Thus, the data near the boundary of training set can be preserved and the others can be disposed through relative density degree. Experimental results show that merely preserving about 20 % of the training set, the performance will not decrease and be better than previous related method. But the model is simpler and the training process is faster.
Vorheriger Artikel A novel soft subspace clustering algorithm with noise detection for high dimensional datasets
Nächster Artikel Enabling efficient approximate nearest neighbor search for outsourced database in cloud computing

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Literatur
Alcala-Fdez J, Sanchez L, Garcia S, del Jesus MJ, Ventura S, Garrell JM, Herrera F (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13(3):307–318
Alcalá J, Fernández A, Luengo J, Derrac J, García S, Sánchez L, Herrera F (2010) Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J Multiple-Valued Logic Soft Comput 17(11):255–287
Barros de Almeida M, de Pádua Braga A, Braga JP (2000) SVM-KM: speeding SVMs learning with a priori cluster selection and k-means. In: Sixth Brazilian symposium on neural networks, 2000. Proceedings. IEEE, New York, pp 162–167
Bertelli L, Yu T, Vu D, Gokturk B (2011) Kernelized structural SVM learning for supervised object segmentation. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, New York, pp 2153–2160
Bradley AP (1997) The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 30(7):1145–1159CrossRef
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2(3):27
Chen Y, Zhou XS, Huang TS (2001) One-class SVM for learning in image retrieval. In: 2001 international conference on image processing, 2001. Proceedings, vol 1. IEEE, New York, pp 34–37
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeCrossRefMATH
Cui J, An Z, Guo Y, Zhou S (2010) Efficient nearest neighbor query based on extended B+-tree in high-dimensional space. Pattern Recognit Lett 31(12):1740–1748CrossRef
Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30MathSciNetMATH
Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, New YorkMATH
Ercan S, Kayakutlu G (2014) Patent value analysis using support vector machines. Soft Comput 18(2):313–328CrossRef
Fan RE, Chen PH, Lin CJ (2005) Working set selection using second order information for training support vector machines. J Mach Learn Res 6:1889–1918MathSciNetMATH
Friedman JH, Bentley JL, Finkel RA (1977) An algorithm for finding best matches in logarithmic expected time. ACM Trans Math Softw (TOMS) 3(3):209–226CrossRefMATH
García S, Fernández A, Luengo J, Herrera F (2010) Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf Sci 180(10):2044–2064CrossRef
Gionis A, Indyk P, Motwani R (1999) Similarity search in high dimensions via hashing. VLDB 99:518–529
Grother PJ, Candela GT, Blue JL (1997) Fast implementations of nearest neighbor classifiers. Pattern Recognit 30(3):459–465CrossRef
Gumus E, Kilic N, Sertbas A, Ucan ON (2010) Evaluation of face recognition techniques using PCA, wavelets and SVM. Exp Syst Appl 37(9):6404–6408CrossRef
Kazmi SB, Jaffar MA (2012) Wavelets-based facial expression recognition using a bank of support vector machines. Soft Comput 16(3):369–379CrossRef
Koggalage R, Halgamuge S (2004) Reducing the number of training samples for fast support vector machine classification. Neural Inf Process Lett Rev 2(3):57–65
Kruskal WH, Wallis WA (1952) Use of ranks in one-criterion variance analysis. J Am Stat Assoc 47(260):583–621CrossRefMATH
Lee K, Kim DW, Lee KH, Lee D (2007) Density-induced support vector data description. IEEE Trans Neural Netw 18(1):284–289CrossRef
Li Y (2011) Selecting training points for one-class support vector machines. Pattern Recognit Lett 32(11):1517–1522CrossRef
Liu T, Moore AW, Yang K, Gray AG (2004) An investigation of practical approximate nearest neighbor algorithms. In: Advances in neural information processing systems. MIT Press, Cambridge, pp 825–832
Mahadevan S, Shah SL (2009) Fault detection and diagnosis in process data using one-class support vector machines. J Process Contr 19(10):1627–1639CrossRef
Ma J, Perkins S (2003) Time-series novelty detection using one-class support vector machines. In: Proceedings of the international joint conference on neural networks, 2003, vol 3. IEEE, New York, pp 1741–1745
Manevitz LM, Yousef M (2002) One-class SVMs for document classification. J Mach Learn Res 2:139–154
Metz CE (1978) Basic principles of ROC analysis. In: Seminars in nuclear medicine, vol 8, no 4. WB Saunders, Philadelphia, pp 283–298
Niu XX, Suen CY (2012) A novel hybrid CNN-SVM classifier for recognizing handwritten digits. Pattern Recognit 45(4):1318–1325CrossRef
Onoda T, Murata H, Yamada S (2006) Non-relevance feedback document retrieval based on one class SVM and SVDD. In: International joint conference on neural networks, 2006. IJCNN’06. IEEE, New York, pp 1212–1219
Osuna E, Freund R, Girosi F (1997a) An improved training algorithm for support vector machines. In: Neural networks for signal processing VII. Proceedings of the 1997 IEEE workshop. IEEE, New York, pp 276–285
Osuna E, Freund R, Girosi F (1997b) Training support vector machines: an application to face detection. In: 1997 IEEE Computer Society conference on computer vision and pattern recognition, 1997. Proceedings. IEEE, New York, pp 130–136
Panda N, Chang EY, Wu G (2006) Concept boundary detection for speeding up SVMs. In: Proceedings of the 23rd international conference on Machine learning. ACM, Toronto, pp 681–688
Pimentel MA, Clifton DA, Clifton L, Tarassenko L (2014) A review of novelty detection. Signal Process 99:215–249CrossRef
Platt J (1999) Fast training of support vector machines using sequential minimal optimization. In: Advances in kernel methods-support vector learning, vol 1999. MIT Press, Cambridge, pp 185–208
Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471CrossRefMATH
Shin H, Cho S (2007) Neighborhood property-based pattern selection for support vector machines. Neural Comput 19(3):816–855CrossRefMATH
Songfeng Z, Xiaofeng L, Nanning Z, Weipu X (2003) Unsupervised clustering based reduced support vector machines. In: 2003 IEEE international conference on acoustics, speech, and signal processing, 2003. Proceedings (ICASSP’03), vol 2. IEEE, New York, pp II-821
Su CT, Chen YC (2012) Rule extraction algorithm from support vector machines and its application to credit screening. Soft Comput 16(4):645–658CrossRef
Tax DM, Duin RP (1999) Support vector domain description. Pattern Recognit Lett 20(11):1191–1199CrossRef
Vapnik V (2000) The nature of statistical learning theory. Springer Science & Business Media, BerlinCrossRefMATH
Wang D, Shi L (2008) Selecting valuable training samples for SVMs via data structure analysis. Neurocomputing 71(13):2772–2781CrossRef
Wang J, Neskovic P, Cooper LN (2007) Selecting data for fast support vector machines training. In: Trends in neural computation. Springer, Berlin, pp 61–84
Wu Y, Wei B, Liu H, Li T, Rayner S (2011) MiRPara: a SVM-based software tool for prediction of most probable microRNA coding regions in genome scale sequences. BMC Bioinf 12(1):107CrossRef
Zhang Y, Meratnia N, Havinga P (2009) Adaptive and online one-class support vector machine-based outlier detection techniques for wireless sensor networks. In: International conference on advanced information networking and applications workshops, 2009. WAINA’09. IEEE, New York, pp 990–995
Zhiqiang G, Huaiqing W, Quan L (2013) Financial time series forecasting using LPP and SVM optimized by PSO. Soft Comput 17(5):805–818CrossRef
Metadaten
Titel
Relative density degree induced boundary detection for one-class SVM
verfasst von
Fa Zhu
Jian Yang
Sheng Xu
Cong Gao
Ning Ye
Tongming Yin
Publikationsdatum
03.07.2015
Verlag
Springer Berlin Heidelberg
Erschienen in
Soft Computing / Ausgabe 11/2016
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-015-1757-7

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