Incremental data-driven learning of a novelty detection model for one-class classification with application to high-dimensional noisy data

Machine Learning

Volumn 74, Issue 2, 2009, Pages 191-234

  Kassab, Randa ^a   Alexandre, Frédéric ^a  

^a LORIA   (France)

Author keywords

ILoNDF; Neural networks; Novelty detection; One class classification; Text categorization

Indexed keywords

CLASSIFIERS; EDUCATION; LEARNING SYSTEMS; NEURAL NETWORKS; PROGRAMMING THEORY; TEXT PROCESSING;

AUTO-ASSOCIATIVE NEURAL NETWORKS; CLASSIFICATION RESULTS; CO OCCURRENCES; COMPUTATIONAL RESOURCES; HIGH-DIMENSIONAL; HOTELLING'S T; ILONDF; INCREMENTAL DATUM; INTRINSIC CHARACTERISTICS; LEARNING CLASSIFIERS; LEARNING METHODS; MACHINE-LEARNING; NOISY DATUM; NOVELTY DETECTION; ONE-CLASS CLASSIFICATION; POSITIVE DATUM; REAL-WORLD; RELATIVE FREQUENCIES; REUTERS; SVM CLASSIFIERS; TEXT CATEGORIZATION; TEXT CORPORA; TRAINING DATUM;

LEARNING ALGORITHMS;

EID: 59449095425     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-008-5092-4     Document Type: Article

References (52)

1. Aeyels, D. (1990). On the dynamic behaviour of the novelty detector and the novelty filter. In B. Bonnard, B. Bride, J. P. Gauthier & I. Kupka (Eds.), Analysis of controlled dynamical systems (pp. 1-10).
2. Baldi, P., Chauvin, Y., & Hornik, K. (1995). Back-propagation and unsupervised learning in linear networks. In Y. Chauvin & D. E. Rumelhart (Eds.), Back propagation: theory, architectures and applications (pp. 389-432). Hillsdale: Lawrence Earlbaum Associates.
3. Ben-Israel, A., & Greville, T. N. E. (2003). Generalized inverse: theory and applications (2nd ed.). New York: Springer.
4. M. W. Brown J. Z. Xiang 1998 Recognition memory: neuronal substrates of the judgement of prior occurrence Progress in Neurobiology 55 149 189
5. Cottrell, G. W., & Munro, P. (1988). Principal components analysis of images via back propagation. In Proceedings of the society of photo-optical instrumentation engineers (pp. 1070-1077).
6. F. Debole F. Sebastiani 2005 An analysis of the relative hardness of Reuters-21578 subsets Journal of the American Society for Information Science and Technology 56 6 584 596
7. Denis, F., Gilleron, R., Laurent, A., & Tommasi, M. (2003). Text classification and co-training from positive and unlabeled examples. In Proceedings of the ICML 2003 workshop: the continuum from labeled to unlabeled data (pp. 80-87).
8. K. P. Detroja R. D. Gudi S. C. Patwardhan 2007 Plant-wide detection and diagnosis using correspondence analysis Control Engineering Practice 15 12 1468 1483
9. Dumais, S., Platt, J., Heckerman, D., & Sahami, M. (1998). Inductive learning algorithms and representations for text categorization. In Proceedings of the 7th international conference on information and knowledge management (CIKM 98) (pp. 148-155).
10. Fletcher, R. (1987). Practical methods of optimization. New York: Wiley.
11. G. P. C. Fung J. X. Yu H. Lu P. S. Yu 2006 Text classification without negative examples revisit IEEE Transactions on Knowledge and Data Engineering 18 1 6 20
12. Greville, T. N. E. (1960). Some applications of the pseudoinverse of a matrix. SIAM Review.
13. J. E. Jackson G. S. Mudholkar 1979 Control procedures for residuals associated with principal component analysis Technometrics 21 3 341 349
14. N. Japkowicz 2001 Supervised versus unsupervised binary-learning by feedforward neural networks Machine Learning 42 1-2 97 122
15. Japkowicz, N., Myers, C., & Gluck, M. A. (1995). A novelty detection approach to classification. In Proceedings of the fourteenth joint conference on artificial intelligence (pp. 518-523).
16. N. Japkowicz S. J. Hanson M. A. Gluck 2000 Nonlinear autoassociation is not equivalent to PCA Neural Computing 12 3 531 545
17. Joachims, T. (2001). A statistical learning model of text classification for support vector machines. In Proceedings of the conference on research and development in information retrieval (SIGIR) (pp. 128-136).
18. Jolliffe, I. T. (1986). Principal component analysis. New York: Springer.
19. Kambhaltla, N., & Leen, T. K. (1994). Fast non-linear dimension reduction. In J. D. Cowan, G. Tesauro & J. Alspector (Eds.), Advances in neural information processing systems (Vol. 6, pp. 152-159).
20. Kassab, R., & Lamirel, J. C. (2006). A new approach to intelligent text filtering based on novelty detection. In The 17th Australian database conference (pp. 149-156), Tasmania, Australia.
21. Kassab, R., & Lamirel, J. C. (2007). Towards a synthetic analysis of user's information need for more effective personalized filtering services. In The 22nd ACM symposium on applied computing special track on information access and retrieval (SAC-IAR) (pp. 852-859).
22. 2 statistic AUTOMATIKA-Journal for Control, Measurement, Electronics, Computing and Communications 43 1-2 29 37
23. Kohonen, T. (1989). Self organisation and associative memory (3rd ed.). New York: Springer.
24. T. Kohonen E. Oja 1976 Fast adaptive formation of orthogonalizing filters and associative memory in recurrent networks of neuron-like elements Biological Cybernetics 21 85 95
25. H. Lee S. Cho 2006 Application of LVQ to novelty detection using outlier training data Pattern Recognition Letters 27 13 1572 1579
26. Lewis, D. D. (1991). Evaluating text categorization. In Proceedings of speech and natural language workshop (pp. 312-318). San Mateo: Morgan Kaufmann.
27. Li, X., & Liu, B. (2003). Learning to classify texts using positive and unlabeled data. In Proceedings of the 18th international joint conference on artificial intelligence (IJCAI-03) (pp. 587-594).
28. Liu, B., Dai, Y., Li, X., Lee, W. S., & Yu, P. S. (2003). Building text classifiers using positive and unlabeled examples. In International conference on data mining (pp. 179-186).
29. L. M. Manevitz M. Yousef 2001 One-class SVMs for document classification Journal of Machine Learning Research 2 139 154
30. M. Markou S. Singh 2003 Novelty detection: a review-part 1: statistical approaches Signal Processing 83 12 2481 2497
31. M. Markou S. Singh 2003 Novelty detection: a review-part 2: neural network based approaches Signal Processing 83 12 2499 2521
32. Marsland, S., Nehmzow, U., & Shapiro, J. (2000). A real-time novelty detector for a mobile robot. In European advanced robotics systems masterclass and conference. Salford: AAAI Press.
33. M. T. Noda I. Makino T. Saito 1997 Algebraic methods for computing a generalized inverse ACM SIGSAM Bulletin 31 3 51 52
34. Oja, E. (1991). Data compression, feature extraction, and autoassociation in feedforward neural networks. In K. Kohonen, et al. (Eds.), Artificial neural networks (pp. 737-745).
35. R. Penrose 1955 A generalized inverse for matrices Proceedings of the Cambridge Philosophical Society 52 406 413
36. B. Raskutti A. Kowalczyk 2004 Extreme re-balancing for SVMs: a case study SIGKDD Explorer Newsletter 6 1 60 69
37. van Rijsbergen, C. J. (1979). Information retrieval. London: Butterworths.
38. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representation by error propagation. In D. E. Rumelhart & J. L. McClelland (Eds.), Parallel distributed processing: explorations in the microstructures of cognition (pp. 318-362). Cambridge: MIT Press.
39. Rüping, S. (2000). mySVM-Manual. Universitat Dortmund, Lehrstuhl Informatik VIII, http://www-ai.cs.uni-dortmund.de/SOFTWARE/MYSVM/.
40. Salton, G. (1971). The SMART retrieval system: experiments in automatic document processing. Englewood Cliffs: Prentice Hall.
41. G. Salton C. Buckley 1988 Term weighting approaches in automatic text retrieval Information Processing and Management 24 5 513 523
42. B. Schölkopf J. Platt J. Shawe-Taylor A. J. Smola R. C. Williamson 2001 Estimating the support of a high-dimensional distribution Neural Computation 13 7 1443 1471
43. Schwab, I., Pohl, W., & Koychev, I. (2000). Learning to recommend from positive evidence. In Proceedings of intelligent user interfaces (pp. 241-247). New York: ACM Press.
44. F. Sebastiani 2002 Machine learning in automated text categorization ACM Computing Surveys 34 1 1 47
45. S. Sirois D. Mareshal 2004 An interacting systems model of infant habituation Journal of Cognitive Neuroscience 16 8 1352 1362
46. D. M. J. Tax R. P. W. Duin 2001 Uniform object generation for optimizing one-class classifiers Journal of Machine Learning Research 2 155 173
47. S. Valle W. Li S. J. Qin 1999 Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods Industrial & Engineering Chemistry Research 38 11 4389 4401
48. Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.
49. Y. Yang S. Slattery R. Ghani 2002 A study of approaches to hypertext categorization Journal of Intelligent Information Systems 18 2-3 219 241
50. Yu, H., Zhai, C., & Han, J. (2003). Text classification from positive and unlabeled documents. In Proceedings of the twelfth international conference on information and knowledge management (pp. 232-239). New York: ACM Press.
51. H. Yu J. Han K. C. C. Chang 2004 PEBL: Web page classification without negative examples IEEE Transactions on Knowledge and Data Engineering 16 1 70 81
52. Žižka, J., Hroza, J., Pouliquen, B., Ignat, C., & Steinberger, R. (2006). The selection of electronic text documents supported by only positive examples. In Proceedings of the 8th international conference on the statistical analysis of textual data (JADT) (pp. 19-21).