1 research outputs found
Minimum Variance Embedded Auto-associative Kernel Extreme Learning Machine for One-class Classification
One-class classification (OCC) needs samples from only a single class to
train the classifier. Recently, an auto-associative kernel extreme learning
machine was developed for the OCC task. This paper introduces a novel extension
of this classifier by embedding minimum variance information within its
architecture and is referred to as VAAKELM. The minimum variance embedding
forces the network output weights to focus in regions of low variance and
reduces the intra-class variance. This leads to a better separation of target
samples and outliers, resulting in an improvement in the generalization
performance of the classifier. The proposed classifier follows a
reconstruction-based approach to OCC and minimizes the reconstruction error by
using the kernel extreme learning machine as the base classifier. It uses the
deviation in reconstruction error to identify the outliers. We perform
experiments on 15 small-size and 10 medium-size one-class benchmark datasets to
demonstrate the efficiency of the proposed classifier. We compare the results
with 13 existing one-class classifiers by considering the mean F1 score as the
comparison metric. The experimental results show that VAAKELM consistently
performs better than the existing classifiers, making it a viable alternative
for the OCC task