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2009

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

10 years 12 days ago
Analysis of the Effect of Unexpected Outliers in the Classification of Spectroscopy Data
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 characterised by the data, whereas in many applications, training data for some classes may be entirely absent, rare, or statistically unrepresentative. We evaluate onesided classifiers as an alternative, since they assume that only one class (the target) is well characterised. 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 wellknown binary classification algorithms, and conclude that the one-sided classifier is more robust to unexpected outliers. Key words: One-Sided, O...
Frank G. Glavin, Michael G. Madden
Added 16 Feb 2011
Updated 16 Feb 2011
Type Journal
Year 2009
Where AICS
Authors Frank G. Glavin, Michael G. Madden
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