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This dissertation investigates the use of one-sided classification algorithmsin the application of separating hazardous chlorinated solvents from othermaterials, based on their Raman spectra. The experimentation is carried outusing a new one-sided classification toolkit that was designed and developedfrom the ground up. In the one-sided classification paradigm, the objective isto separate elements of the target class from all outliers. These one-sidedclassifiers are generally chosen, in practice, when there is a deficiency ofsome sort in the training examples. Sometimes outlier examples can be rare,expensive to label, or even entirely absent. However, this author would like tonote that they can be equally applicable when outlier examples are plentifulbut nonetheless not statistically representative of the complete outlierconcept. It is this scenario that is explicitly dealt with in this researchwork. In these circumstances, one-sided classifiers have been found to be morerobust that conventional multi-class classifiers. The term "unexpected"outliers is introduced to represent outlier examples, encountered in the testset, that have been taken from a different distribution to the training setexamples. These are examples that are a result of an inadequate representationof all possible outliers in the training set. It can often be impossible tofully characterise outlier examples given the fact that they can represent theimmeasurable quantity of "everything else" that is not a target. The findingsfrom this research have shown the potential drawbacks of using conventionalmulti-class classification algorithms when the test data come from a completelydifferent distribution to that of the training samples.

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