  This paper presents an online learning with regularized kernel based
one-class extreme learning machine (ELM) classifier and is referred as online
RK-OC-ELM. The baseline kernel hyperplane model considers whole data in a
single chunk with regularized ELM approach for offline learning in case of
one-class classification (OCC). Further, the basic hyper plane model is adapted
in an online fashion from stream of training samples in this paper. Two
frameworks viz., boundary and reconstruction are presented to detect the target
class in online RKOC-ELM. Boundary framework based one-class classifier
consists of single node output architecture and classifier endeavors to
approximate all data to any real number. However, one-class classifier based on
reconstruction framework is an autoencoder architecture, where output nodes are
identical to input nodes and classifier endeavor to reconstruct input layer at
the output layer. Both these frameworks employ regularized kernel ELM based
online learning and consistency based model selection has been employed to
select learning algorithm parameters. The performance of online RK-OC-ELM has
been evaluated on standard benchmark datasets as well as on artificial datasets
and the results are compared with existing state-of-the art one-class
classifiers. The results indicate that the online learning one-class classifier
is slightly better or same as batch learning based approaches. As, base
classifier used for the proposed classifiers are based on the ELM, hence,
proposed classifiers would also inherit the benefit of the base classifier i.e.
it will perform faster computation compared to traditional autoencoder based
one-class classifier.
