Experimental results show that LOF and SVDD are the two best-performing methods. It is concluded that both fields offer outlier-detection methods that are competitive in performance and that bridging the gap between both fields may facilitate the development of outlier-detection methods.
|Title of host publication||Proceedings of the Eight International Conference on Machine Learning and Applications|
|Editors||A. Wani, M. Kantardzic, V. Palade, L. Kurgan, Y. Qi|
|Place of Publication||Miami, FL, USA|
|Publication status||Published - 2009|
Janssens, J. H. M., Flesch, I., & Postma, E. O. (2009). Outlier detection with one-class classifiers from ML and KDD. In A. Wani, M. Kantardzic, V. Palade, L. Kurgan, & Y. Qi (Eds.), Proceedings of the Eight International Conference on Machine Learning and Applications (pp. 147-153). ICMLA. http://www.icmla-conference.org/icmla09/