  This study is about inducing classifiers using data that is imbalanced, with
a minority class being under-represented in relation to the majority classes.
The first section of this research focuses on the main characteristics of data
that generate this problem. Following a study of previous, relevant research, a
variety of artificial, imbalanced data sets influenced by important elements
were created. These data sets were used to create decision trees and rule-based
classifiers. The second section of this research looks into how to improve
classifiers by pre-processing data with resampling approaches. The results of
the following trials are compared to the performance of distinct pre-processing
re-sampling methods: two variants of random over-sampling and focused
under-sampling NCR. This paper further optimises class imbalance with a new
method called Sparsity. The data is made more sparse from its class centers,
hence making it more homogenous.
