254
Views
2
CrossRef citations to date
0
Altmetric
Articles

Influence of Distance Measures on the Effectiveness of One-Class Classification Ensembles

&

REFERENCES

  • Alpaydin, E. 1999. Combined 5 × 2 cv f test for comparing supervised classification learning algorithms. Neural Computation 11(8):1885–1892.
  • Bicego, M. and M. A. T. Figueiredo. 2009. Soft clustering using weighted one-class support vector machines. Pattern Recognition 42(1):27–32.
  • Bishop, C. M. 1994. Novelty detection and neural network validation. Vision, Image and Signal Processing 141(4): 217–222.
  • Cha, S. H. 2007. Comprehensive study on distance/similarity measures between probability density functions. International Journal of Mathematical Modeling and Methods in Applied Sciences 1(4):300–307.
  • Cheplygina, V., and D. M. J. Tax. 2011. Pruned random subspace method for one-class classifiers. Lecture Notes in Computer Science 6713:96–105. Berlin, Heldelberg: Springer.
  • Cyganek, B. 2012. One-class support vector ensembles for image segmentation and classification. Journal of Mathematical Imaging and Vision 42(2–3):103–117.
  • Domeniconi, C., J. Peng, and D. Gunopulos. 2002. Locally adaptive metric nearest-neighbor classification. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(9):1281–1285.
  • Ho, T. K. 1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8):832–844.
  • Hodge, V. J., and J. Austin. 2004. A survey of outlier detection methodologies. Artificial Intelligence Review 22(2):85–126.
  • Hu, Q., D. Yu, and Z. Xie. 2008. Neighborhood classifiers. Expert Systems with Applications 34(2):866–876.
  • Jain, A. K., R. P. W. Duin, and J. Mao. 2000. Statistical pattern recognition: A review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1):4–37.
  • Koch, M. W., M. M. Moya, L. D. Hostetler, and R. J. Fogler. 1995. Cueing, feature discovery, and one-class learning for synthetic aperture radar automatic target recognition. Neural Networks 8(7–8):1081–1102.
  • Krawczyk, B., and M. Woźniak. 2012a. Combining diverse one-class classifiers. Lecture Notes on Artificial Intelligence 7209, (Issue PART 2):590–601.
  • Krawczyk B. and M. Woźniak. 2012b. Experiments on distance measures for combining one-class classifiers. In Proceedings of the Fedcisis 2012 Conference, 88–92, 9–12 September, IEEE, Wroclaw, Poland.
  • Li, C., and Y. Zhang. 2008. Bagging one-class decision trees. In Proceedings of 5th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2008), 2:420–423.
  • Mazhelis, O., and S. Puuronen. 2004. Combining one-class classifiers for mobile-user substitution detection. Paper presented at the ICEIS 2004—Proceedings of the Sixth International Conference on Enterprise Information Systems, 130–137, 14–17 April, Universidade Portucalense, Porto, Portugal.
  • R Development Core Team. 2008. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
  • Scholkopf, B. and A. J. Smola. 2002. Learning with kernels: support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning. Cambridge, MA, USA: MIT Press.
  • SIAM. Proceedings of the Eleventh SIAM International Conference on Data Mining, SDM 2011, April 28–30, 2011, Mesa, Arizona, USA. http://tunedit.org/challenge/QSAR. SIAM Omnipress, 2011.
  • Tax, D. M. J. and R. P. W. Duin. 2001. Combining one-class classifiers. In Proceedings of the Second International Workshop on Multiple Classifier Systems, MCS ’01, 299–308. London, UK: Springer-Verlag.
  • Tax, D. M. J. and R. P. W. Duin. 2005. Characterizing one-class datasets. In Proceedings of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, 21–26.
  • Wilson, D. R., and T. R. Martinez. 1997. Improved heterogeneous distance functions. Journal of Artificial Intelligence Research 6:1–34.
  • Wu, R.-S., and W.-H. Chung. 2009. Ensemble one-class support vector machines for content-based image retrieval. Expert Systems with Applications 36(3):4451–4459.
  • Yeh, C.-Y., Z.-Y. Lee, and S.-J. Lee. 2009. Boosting one-class support vector machines for multi-class classification. Applied Artificial Intelligence 23(4):297–315.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.