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Viewing report
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Classifier Learning for Imbalanced Data. Edition No. 1
VDM Publishing House, Aug 2008, Pages: 180
This work discusses the theoretical abilities of
three commonly used classifier learning methods and
optimization techniques to cope with characteristics
of real-world classification problems, more
specifically varying misclassification costs,
imbalanced data sets and varying degrees of hardness
of class boundaries.
From these discussions a universally applicable
optimization framework is derived that successfully
corrects the error-based inductive bias of classifier
learning methods on image data within the domain of
medical diagnosis.
The framework was designed considering several points
for improvement of common optimization techniques,
such as the modification of the optimization
procedure for inducer-specific parameters, the
modification of input data by an arcing algorithm,
and the combination of classifiers according to
locally-adaptive, cost-sensitive voting schemes.
The framework is designed to make the learning
process cost-sensitive and to enforce more balanced
misclassification costs between classes. Results on
the evaluated domain are promising, while further
improvements can be expected after some modifications
to the framework.
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