A new random forest method for one-class classification

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7626 LNCS, Issue , 2012, Pages 282-290

  Désir, Chesner ^a   Bernard, Simon ^b   Petitjean, Caroline ^a   Heutte, Laurent ^a  

^a UNIVERSITÉ DE ROUEN   (France)

^b UNIVERSITY OF LIÈGE   (Belgium)

Author keywords

decision trees; ensemble methods; One class classification; outlier generation; random forests

Indexed keywords

DATA SETS; ENSEMBLE LEARNING; ENSEMBLE METHODS; FEATURE SPACE; GAUSSIAN DENSITY; GAUSSIAN MIXTURE MODEL; ONE-CLASS CLASSIFICATION; OUTLIER GENERATION; PARZEN ESTIMATORS; RANDOM FOREST ALGORITHM; RANDOM FOREST METHODS; RANDOM FORESTS;

ALGORITHMS; PATTERN RECOGNITION; STATISTICS; SYNTACTICS;

DECISION TREES;

EID: 84868134628     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-34166-3_31     Document Type: Conference Paper

References (16)

1. Hempstalk, K., Frank, E., Witten, I.: One-Class Classification by Combining Density and Class Probability Estimation. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part I. LNCS (LNAI), vol. 5211, pp. 505-519. Springer, Heidelberg (2008)
2. Scholkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., Williamson, R.: Estimating the support of a high-dimensional distribution. Neural Computation 13(7), 1443-1471 (2001)
3. Tarassenko, L., Clifton, D., Bannister, P., King, S., King, D.: Novelty detection. Encyclopedia of Structural Health Monitoring (2009)
4. Khan, S., Madden, M.: A survey of recent trends in one class classification. Artificial Intelligence and Cognitive Science, 188-197 (2010)
5. Tax, D., Duin, R.: Combining One-Class Classifiers. In: Kittler, J., Roli, F. (eds.) MCS 2001. LNCS, vol. 2096, pp. 299-308. Springer, Heidelberg (2001)
6. Dietterich, T.: Ensemble Methods in Machine Learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1-15. Springer, Heidelberg (2000)
7. Breiman, L.: Random forests. Machine Learning 45(1), 5-32 (2001)
8. Robnik-Sikonja, M.: Improving Random Forests. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 359-370. Springer, Heidelberg (2004)
9. Bernard, S., Heutte, L., Adam, S.: Forest-rk: A new random forest induction method. In: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, pp. 430-437 (2008)
10. Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Machine Learning 63(1), 3-42 (2006)
11. Ho, T.: The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(8), 832-844 (1998)
12. Tax, D., Ypma, A., Duin, R.: Support vector data description applied to machine vibration analysis. In: Proc. 5th Annual Conference of the Advanced School for Computing and Imaging, Heijen, NL, Citeseer (1999)
13. Blake, C., Merz, C.: Uci repository of machine learning databases. Department of Information and Computer Science, vol. 55. University of California, Irvine (1998), http://www.ics.uci.edu/mlearn/mlrepository.html
14. Baldi, P., Brunak, S., Chauvin, Y., Andersen, C., Nielsen, H.: Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16(5), 412-424 (2000)
15. Duin, R.: PRTools version 3.0: A matlab toolbox for pattern recognition. In: Proc. of SPIE, Citeseer (2000)
16. Bernard, S., Heutte, L., Adam, S.: Influence of Hyperparameters on Random Forest Accuracy. In: Benediktsson, J.A., Kittler, J., Roli, F. (eds.) MCS 2009. LNCS, vol. 5519, pp. 171-180. Springer, Heidelberg (2009)