On The Construction of Extreme Learning Machine for Online and Offline One-Class Classification - An Expanded Toolbox

TL;DR

This paper introduces six novel Extreme Learning Machine-based one-class classifiers supporting online and offline learning, tested on artificial and benchmark datasets, outperforming traditional methods and expanding the DD toolbox for OCC.

Contribution

The paper presents new ELM and OSELM-based OCC methods with both reconstruction and boundary approaches, supporting online and offline learning, and enhances the DD toolbox for OCC.

Findings

Proposed classifiers outperform ten traditional OCC methods.

Kernel feature mapping approaches show improved boundary detection.

Online classifiers perform well with different node types.

Abstract

One-Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines. But, traditional methods based one-class classifiers are very time consuming due to its iterative process and various parameters tuning. In this paper, we present six OCC methods based on extreme learning machine (ELM) and Online Sequential ELM (OSELM). Our proposed classifiers mainly lie in two categories: reconstruction based and boundary based, which supports both types of learning viz., online and offline learning. Out of various proposed methods, four are offline and remaining two are online methods. Out of four offline methods, two methods perform random feature mapping and two methods perform kernel feature mapping. Kernel feature mapping based approaches have been tested with RBF kernel and online version of one-class classifiers are tested with both types of nodes viz., additive and RBF. It is well known fact that threshold decision is a crucial factor in case of OCC, so, three different threshold deciding criteria have been employed so far and analyses the effectiveness of one threshold deciding criteria over another. Further, these methods are tested on two artificial datasets to check there boundary construction capability and on eight benchmark datasets from different discipline to evaluate the performance of the classifiers. Our proposed classifiers exhibit better performance compared to ten traditional one-class classifiers and ELM based two one-class classifiers. Through proposed one-class classifiers, we intend to expand the functionality of the most used toolbox for OCC i.e. DD toolbox. All of our methods are totally compatible with all the present features of the toolbox.