Efficient Training of One Class Classification-SVMs
Isaac Amornortey Yowetu Nana Kena Frempong
This study examines the use of a highly effective training method to conduct
one-class classification. The existence of both positive and negative examples
in the training data is necessary to develop an effective classifier in common
binary classification scenarios. Unfortunately, this criteria is not met in
many domains. Here, there is just one class of examples. Classification
algorithms that learn from solely positive input have been created to deal with
this setting. In this paper, an effective algorithm for dual soft-margin
one-class SVM training is presented. Our approach makes use of the Augmented
Lagrangian (AL-FPGM), a variant of the Fast Projected Gradient Method. The FPGM
requires only first derivatives, which for the dual soft margin OCC-SVM means
computing mainly a matrix-vector product. Therefore, AL-FPGM, being
computationally inexpensive, may complement existing quadratic programming
solvers for training large SVMs. We extensively validate our approach over
real-world datasets and demonstrate that our strategy obtains statistically
significant results.