Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data

TL;DR

This paper compares one-sided classifiers to traditional multi-class algorithms for spectroscopy data, demonstrating that one-sided methods are more robust to unexpected outliers in identifying chlorinated solvents.

Contribution

It introduces a new one-sided classification toolkit and shows its effectiveness over binary classifiers in handling unrepresentative outliers.

Findings

One-sided classifiers outperform binary classifiers in robustness.

The new toolkit effectively handles unexpected outliers.

One-sided k-NN shows superior performance in spectroscopy classification.

Abstract

Multi-class classification algorithms are very widely used, but we argue that they are not always ideal from a theoretical perspective, because they assume all classes are characterized by the data, whereas in many applications, training data for some classes may be entirely absent, rare, or statistically unrepresentative. We evaluate one-sided classifiers as an alternative, since they assume that only one class (the target) is well characterized. We consider a task of identifying whether a substance contains a chlorinated solvent, based on its chemical spectrum. For this application, it is not really feasible to collect a statistically representative set of outliers, since that group may contain \emph{anything} apart from the target chlorinated solvents. Using a new one-sided classification toolkit, we compare a One-Sided k-NN algorithm with two well-known binary classification algorithms, and conclude that the one-sided classifier is more robust to unexpected outliers.