1 research outputs found
A One-Sided Classification Toolkit with Applications in the Analysis of Spectroscopy Data
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.Comment: Research Master's Dissertation. National University of Ireland,
Galway. (2009