Skip to main content
SpringerLink
Log in

Fuzzy One-Class Extreme Auto-encoder

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

A novel one class classification (OCC) algorithm called fuzzy one class extreme auto-encoder (FOCEAE) is presented in this article. The algorithm combines the precision of probability density estimation and the generalization of neural networks to accurately generate the compact bound for the target class cases. Firstly, a K-nearest-neighbors non-parametric probability density estimation-alike strategy is used to estimate the relative densities of all target class training objects, then the relative densities are transformed to be the fuzzy coefficients for further training fuzzy extreme learning machine (FELM) model. Specifically, considering there are only one-class instances, FELM is trained in the form of auto-encoder, i.e., each input equals to be the expected output of the network. Finally, the bound (i.e., the threshold) of the target class cases is determined by calculating and ranking the reconstructed errors of all training instances. We show the effectiveness and superiority of the proposed FOCEAE algorithm by comparing it with some benchmark OCC algorithms on a mass of data sets in terms of both F-measure and G-mean metrics. The statistical results also indicate that the proposed algorithm performs significantly better than some conventional ones.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Yu H (2005) Single-class classification with mapping convergence. Mach Learn 61:49–60

    Article  Google Scholar 

  2. Lee K, Kim DW, Lee KH, Lee D (2007) Density-induced support vector data description. IEEE Trans Neural Netw 18:284–289

    Article  Google Scholar 

  3. Hodge V, Austin J (2004) A survey of outlier detection methodologies. Artif Intell Rev 22:85–126

    Article  MATH  Google Scholar 

  4. Ernst M, Haesbroeck G (2017) Comparison of local outlier detection techniques in spatial multivariate data. Data Min Knowl Disc 31:371–399

    Article  MathSciNet  Google Scholar 

  5. Pimentel MA, Clifton DA, Clifton L, Tarassenko L (2014) A review of novelty detection. Sig Process 99:215–249

    Article  Google Scholar 

  6. Khan SS, Madden MG (2014) One-class classification taxonomy of study and review of techniques. Knowl Eng Rev 29:345–374

    Article  Google Scholar 

  7. Luca S, Clifton D, Vanrumste B (2016) One-class classification of point patterns of extremes. J Mach Learn Res 17:1–21

    MathSciNet  MATH  Google Scholar 

  8. Mena L, Gonzalez JA (2009) Symbolic one-class learning from imbalanced datasets: applications in medical diagnosis. Int J Artif Intell Tools 18:273–309

    Article  Google Scholar 

  9. Yu H, Han J, Chang KC (2004) PEBL: web page classification without negative examples. IEEE Trans Knowl Data Eng 16:70–81

    Article  Google Scholar 

  10. Kennedy K, Mac Namee B, Delany SJ (2009) Credit scoring: solving the low default portfolio problem using one-class classification. In: Proceedings of the 20th Irish conference on artificial intelligence and cognitive science, pp 168–177

  11. Skabar A (2003) Single-class classifier learning using neural networks: an application to the prediction of mineral deposits. In: Proceedings of the 2003 international conference on machine learning and cybernetics, vol. 4, pp 2127–2132

  12. Pan R, Zhou Y, Cao B, Liu NN, Lukose R, Scholz M, Yang Q (2008) One-class collaborative filtering. In: Proceedings of 2008 IEEE international conference on data mining, pp 502–511

  13. Burez J, Van den Poel D (2009) Handling class imbalance in customer churn prediction. Expert Syst Appl 36:4626–4636

    Article  Google Scholar 

  14. Coh KS, Chang EY, Li B (2005) Using one-class and two-class SVMs for multiclass image annotation. IEEE Trans Knowl Data Eng 17:1333–1346

    Article  Google Scholar 

  15. Maldonado S, Montecinos C (2014) Robust classification of imbalanced data using one-class and two-class SVM-based multiclassifiers. Intell Data Anal 18:95–112

    Article  Google Scholar 

  16. Duin R (1976) On the choice of smoothing parameters for Parzen estimators of probability density functions. IEEE Trans Comput 25:1175–1179

    Article  MATH  Google Scholar 

  17. Japkowicz N (1999) Concept learning in the absence of counter examples, an autoassociation-based approach to classification. Dissertation, State University of New Jersey

  18. Leng Q, Qi H, Miao J, Zhu W, Su G (2015) One-class classification with extreme learning machine. Math Probl Eng 2015:412957

    Article  MathSciNet  MATH  Google Scholar 

  19. Chawla S, Glonis A (2013) K-means: a unified approach to clustering and outlier detection. In: Proceedings of the 2013 SIAM international conference on data mining, pp 189–197

  20. Tax D, Duin R (2000) Data description in subspaces. In: Proceedings of the 15th international conference on pattern recognition, pp 2672–2675

  21. Scholkopf B, Platt JC, Shawe-Taylor J (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13:1443–1471

    Article  MATH  Google Scholar 

  22. Tax D, Duin R (2004) Support vector data description. Mach Learn 54:45–66

    Article  MATH  Google Scholar 

  23. Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24:5659–5670

    Article  MathSciNet  MATH  Google Scholar 

  24. Liu W, Ma T, Xie Q, Tao D, Cheng J (2017) LMAE: a large margin auto-encoder for classification. Sig Process 141:137–143

    Article  Google Scholar 

  25. Tao D, Guo Y, Li Y, Gao X (2018) Tensor rank preserving discriminant analysis for facial recognition. IEEE Trans Image Process 27:325–334

    Article  MathSciNet  MATH  Google Scholar 

  26. Yu J, Hong C, Rui Y, Tao D (2018) Multi-task autoencoder model for recovering human poses. IEEE Trans Ind Electron. https://doi.org/10.1109/tie.2017.2739691

    Google Scholar 

  27. Yu J, Yang X, Gao F, Tao D (2017) Deep multimodal distance metric learning using click constraints for image ranking. IEEE Trans Cybern 47:2014–2024

    Google Scholar 

  28. Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501

    Article  Google Scholar 

  29. Huang GB, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 42:513–529

    Article  Google Scholar 

  30. Huang GB, Wang DH, Lan Y (2011) Extreme learning machine: a survey. Int J Mach Learn Cybern 2:107–122

    Article  Google Scholar 

  31. Huang G, Huang GB, Song S, You K (2015) Trends in extreme learning machine: a review. Neural Netw 61:32–48

    Article  MATH  Google Scholar 

  32. Zhang WB, Ji HB (2013) Fuzzy extreme learning machine for classification. Electron Lett 49:448–450

    Article  Google Scholar 

  33. Fletcher R (1981) Practical methods of optimization, constrained optimization, vol 2. Wiley, London

    MATH  Google Scholar 

  34. Wang Q, Kulkarni SR, Verdú S (2009) Divergence estimation for multidimensional densities via k-nearest-neighbor distances. IEEE Trans Inf Theory 55:2392–2405

    Article  MathSciNet  MATH  Google Scholar 

  35. Mack YP, Rosenblatt M (1979) Multivariate k-nearest neighbor density estimates. J Multivar Anal 9:1–15

    Article  MathSciNet  MATH  Google Scholar 

  36. Fukunaga K, Hostetler L (1973) Optimization of k nearest neighbor density estimates. IEEE Trans Inf Theory 19:320–326

    Article  MathSciNet  MATH  Google Scholar 

  37. Williams DR, Hinton G (1986) Learning representations by back-propagating errors. Nature 323:533–538

    Article  MATH  Google Scholar 

  38. Alcalá-Fdez J, Sanchez L, Garcia S, del Jesus MJ, Ventura S, Garrell JM, Otero J, Romero C, Bacardit J, Rivas VM, Fernández JC (2009) KEEL: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput 13:307–318

    Article  Google Scholar 

  39. Blake C, Keogh E, Merz CJ (1998) UCI repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, CA, USA. http://www.ics.uci.edu/mlearn/MLRepository.html

  40. Zou Q, Guo M, Liu Y, Wang J (2010) A classification method for class imbalanced data and its application on bioinformatics. Chi J Comput Res Dev 47:1407–1414

    Google Scholar 

  41. He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21:1263–1284

    Article  Google Scholar 

  42. Guo H, Li Y, Shang J, Gu M, Huang Y, Gong B (2017) Learning from class-imbalance data: review of methods and applications. Expert Syst Appl 73:220–239

    Article  Google Scholar 

  43. Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  MATH  Google Scholar 

  44. Garcia S, Fernandez A, Luengo J, Herrera F (2010) Advanced non-parametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: experimental analysis of power. Inf Sci 180:2044–2064

    Article  Google Scholar 

  45. Garcia S, Derrac J, Triguero I, Carmona CJ, Herrera F (2012) Evolutionary-based selection of generalized instances for imbalanced classification. Knowl Based Syst 25:3–12

    Article  Google Scholar 

Download references

Acknowledgements

The work was supported in part by National Natural Science Foundation of China under Grants Nos. 61305058 and 61572242, Natural Science Foundation of Jiangsu Province of China under Grant No. BK20130471, and China Postdoctoral Science Foundation under Grants Nos. 2013M540404 and 2015T80481.

Author information

Authors and Affiliations

  1. School of Computer, Jiangsu University of Science and Technology, No. 2, Mengxi Road, Zhenjiang, 212003, Jiangsu, People’s Republic of China

    Hualong Yu, Dan Sun, Xiaoyan Xi, Xibei Yang, Shang Zheng & Qi Wang

Authors
  1. Hualong Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dan Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiaoyan Xi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xibei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qi Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hualong Yu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, H., Sun, D., Xi, X. et al. Fuzzy One-Class Extreme Auto-encoder. Neural Process Lett 50, 701–727 (2019). https://doi.org/10.1007/s11063-018-9952-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11063-018-9952-z

Keywords

Search

Navigation