Construction of multi-class classifiers by Extreme Learning Machine based one-class classifiers

Abstract

Construction of multi-class classifiers using homogeneous combination of Extreme Learning Machine (ELM) based one-class classifiers have been proposed in this paper. Each class has been trained using individual one-class classifier and any new sample will belong to that class, which will yield maximum value. Proposed methods can be used to detect unknown outliers using multi-class classifiers. Two recently proposed one-class classifiers viz., kernel and random feature mapping based one-class ELM, is extended for multi-class construction in this paper. Further, we construct one-class classifier based multi-class classifier in two ways: with rejection and without rejection of few samples during training. We also perform consistency based model selection for optimal parameters selection in one-class classifier. We have tested the generalization capability of the proposed classifiers on 6 synthetic datasets and two benchmark datasets. © 2016 IEEE.