A One-Sided Classification Toolkit with Applications in the Analysis of Spectroscopy Data

TL;DR

This paper presents a new one-sided classification toolkit applied to Raman spectroscopy data for hazardous solvent detection, demonstrating robustness over traditional multi-class classifiers especially with outliers from different distributions.

Contribution

The research introduces a novel toolkit for one-sided classification and explores its effectiveness in spectroscopic analysis of hazardous materials, addressing challenges with unexpected outliers.

Findings

One-sided classifiers outperform multi-class classifiers with different distribution outliers.

The toolkit effectively separates target chlorinated solvents from other materials.

Conventional classifiers have limitations with outliers from different distributions.

Abstract

This dissertation investigates the use of one-sided classification algorithms in the application of separating hazardous chlorinated solvents from other materials, based on their Raman spectra. The experimentation is carried out using a new one-sided classification toolkit that was designed and developed from the ground up. In the one-sided classification paradigm, the objective is to separate elements of the target class from all outliers. These one-sided classifiers are generally chosen, in practice, when there is a deficiency of some sort in the training examples. Sometimes outlier examples can be rare, expensive to label, or even entirely absent. However, this author would like to note that they can be equally applicable when outlier examples are plentiful but nonetheless not statistically representative of the complete outlier concept. It is this scenario that is explicitly dealt with in this research work. In these circumstances, one-sided classifiers have been found to be more robust that conventional multi-class classifiers. The term "unexpected" outliers is introduced to represent outlier examples, encountered in the test set, that have been taken from a different distribution to the training set examples. These are examples that are a result of an inadequate representation of all possible outliers in the training set. It can often be impossible to fully characterise outlier examples given the fact that they can represent the immeasurable quantity of "everything else" that is not a target. The findings from this research have shown the potential drawbacks of using conventional multi-class classification algorithms when the test data come from a completely different distribution to that of the training samples.