AN ENSEMBLE ANOMALY DETECTION WITH IMBALANCED DATA BASED ON ROBOT VISION

Yongxiong Wang, Shuxin Sun, and Jiandong Zhong

References

  1. [1] O. Duran, K. Althoefer, and D.L. Seneviratne, Automated pipe defect detection and categorization using camera/laser-based profiler and artificial neural network, IEEE Transactions on Automation Science and Engineering, 4(2), 2007, 118–112.
  2. [2] S.K. Sinha and P.W. Fieguth, Neuro-fuzzy network for the classification of buried pipe defects, Automation in Construction, 15(1), 2006, 73–83.
  3. [3] Y. Wang and J. Su, Rapid cascade condition assessment of ductwork via robot vision, Optical Engineering, 51(02), 2012, 027201.1–027201.12.
  4. [4] X.H. Xie, A review of recent advances in surface defect detection using texture analysis techniques, Electronic Letters on Computer Vision and Image Analysis, 7(3), 2008, 1–22.
  5. [5] H. Masnadi-Shiraz and N. Vasconcelos, Cost-sensitive boosting, IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(2), 2011, 294–309.
  6. [6] N. Japkowicz and S. Stephen, The class imbalance problem: A systematic study, Intelligent Data Analysis Journal, 6(5), 2002, 429–450.
  7. [7] H. He and E.A. Garcia, Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering, 21(9), 2009, 99.1263–99.1284.
  8. [8] T. Jo and N. Japkowicz, Class imbalances versus small disjuncts, ACM SIGKDD Explorations Newsletter, 6(1), 2004, 40–49.
  9. [9] B. Lundquist, et al., Assessment, cleaning and restoration of HVAC systems, https://nadca.com/sites/default/files/userfiles/ ACR%202006.pdf, 2006.
  10. [10] J.R. Quinlan, C4.5: Programs for machine learning (Morgan Kaufmann Publishers, 1993).
  11. [11] S.R. Gaddam, V.V. Phoha, and K.S. Balagani, k-Means + ID3: A novel method for supervised anomaly detection by cascading k-means clustering and ID3 decision tree learning methods, IEEE Transactions on Knowledge and Data Engineering, 19(3), 2007, 345–354. 82
  12. [12] V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection: A survey, ACM Computing Surveys, 41(3), 2009, 75–79.
  13. [13] Y. Suna, M.S. Kamela, A.K.C. Wong, et al., Cost-sensitive boosting for classification of imbalanced data, Pattern Recognition, 40(12), 2007, 3358–3378.
  14. [14] R. Longadge, S. Dongre, and L. Malik, Class imbalance problem in data mining review, International Journal of Computer Science & Network, 2(1), 2013, 83–87.
  15. [15] M.A. Tahir, A. Bouridane, and F. Kurugollu, Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier. Pattern Recognition Letters, 28(4), 2007, 438–446.
  16. [16] D.W. Opitz, Feature selection for ensembles, Proc. Sixteenth National Conf. on Artificial Intelligence, Orlando, FL, 1999, 379–384.
  17. [17] S. Kotsiantis, Combining bagging, boosting, rotation forest and random subspace methods, Artificial Intelligence Review, 35(3), 2011, 223–240.
  18. [18] H. Zhang and G. Sun, Feature selection using Tabu search method, Pattern Recognition, 35(3), 2002, 701–711.
  19. [19] G. Amal S., V. Svetha, and W. Geoff, Multi-class pattern classification in imbalanced data, Proc. International Conf. on Pattern Recognition, Istanbul, Turkey, 2010, 2881–2884.

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