Conference Publication Details
Mandatory Fields
Frank G. Glavin, Michael G. Madden
20th Irish Conference on Artificial Intelligence and Cognitive Science
Analysis of the Effects of Unexpected Outliers in the Classification of Spectroscopy Data
In Press
Optional Fields
machine learning, classification, spectroscopy, toolkit
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 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.
Grant Details
Publication Themes
Informatics, Physical and Computational Sciences