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Complexity measurement for dealing with class imbalance problems in classification modelling : a thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy, Massey University, 2012
The class imbalance problem is a challenge in the statistical, machine learn-
ing and data mining domains. Examples include fraud/intrusion detection,
medical diagnosis/monitoring, bioinformatics, text categorization, insurance
claims, and target marketing. The problem with imbalanced data sets is that
the conventional classifiers (both statistical and machine learning algorithms)
aim at maximizing overall accuracy, which is often achieved by allocating all,
or almost all, cases to the majority class. Thus there tends to be bias against
the minority class in class imbalance situations.
Despite numerous algorithms and re-sampling techniques proposed in the
last few decades to tackle imbalanced classification problems, there is no
consistent winning strategy for all data sets (neither in terms of sampling, nor
learning algorithm). Special attention needs to be paid to the data in hand.
In doing so, one should take into account several factors simultaneously: the
imbalance rate, the data complexity, the algorithms and their associated
parameters. As suggested in the literature, mining such datasets can only
be improved by algorithms tailored to data characteristics; therefore it is
important and necessary to do data exploratory analysis before deciding on
a learning algorithm or re-sampling techniques.
In this study, we have developed a framework "Complexity Measurement"
(CM) to explore the connection between the imbalanced data problem and
data complexity. Our study shows that CM is an ideal candidate to be
recognized as a "goodness criterion" for various classifiers, re-sampling and
feature selection techniques in the class imbalance framework. We have used
CM as a meta-learner to choose the classifier and under-sampling strategy
that best fits the situation. We design a systematic over-sampling technique, Over-sampling using Complexity Measurement (OSCM) for dealing
with class overlap. Using OSCM, we do not need to search for an optimal
class distribution in order to get favorable accuracy for the minority class,
since the amount of over-sampling is determined by the complexity; ideally
using CM would detect fine structural differences (class-overlap and small
disjunct) between different classes.Existing feature selection techniques were never meant for class imbalanced data. We propose Feature Selection using Complexity Measurement
(FSCM), which can specifically focus on the minority class, hence those
features (and multivariate interactions between predictors) can be selected,
which form a better model for the minority class.
Methods developed have been applied to real datasets. The results from
imbalanced datasets show that CM, OSCM and FSCM are effective as a systematic way of correcting class imbalance/overlap and improving classifier
performance. Highly predictive models were built; discriminating patterns
were discovered, and automated optimization was proposed. The methodology proposed and knowledge discovered will benefit exploratory data analysis for imbalanced datasets. It may be taken as a judging criterion for new
algorithms and re-sampling techniques. Moreover, new data sets may be
evaluated using our CM criterion in order to build a sensible model.