Les algorithmes de fouille de données à évaluer sur le jeu de données V1
En utilisant Weka¶
to be completed by Sabine and al
Les méthodes de WEKA utilisées par Sabine sont :
- Decision Tree : J48
- Random Forest : RandaomForest
- ANN : Multilayer Perception
Semi-supervisé¶
Co-training (Blum, A., Mitchell, T. Combining labeled and unlabeled data with co-training. COLT: Proceedings of the Workshop on Computational Learning Theory, Morgan Kaufmann, 1998, p. 92-100.)
Détection d'anomalie¶
1) FrAc (K. Noto, C. E. Brodley, D. K. Slonim, Anomaly detection using an ensemble of feature models, in: ICDM, 2010, pp. 953–958.)
2) OSVM (B. S. John, J. C. Platt, J. Shawe-taylor, A. J. Smola, R. C. Williamson, Estimating the support of a high-dimensional distribution, Neural Com- putation 13 (1999) 2001)
3) uLSIF (S. Hido, Y. Tsuboi, H. Kashima, M. Sugiyama, T. Kanamori, Statistical outlier detection using direct density ratio estimation, Knowl. Inf. Syst. 26 (2)
4) SAndCat (ongoing work)