Note: [] Address for correspondence: School of Information Technology and
Engineering, University of Ottawa, Canada
Abstract: Relational databases, with vast amounts of data¨Cfrom financial
transactions, marketing surveys, medical records, to health informatics
observations¨C and complex schemas, are ubiquitous in our society.
Multirelational classification algorithms have been proposed to learn from such
relational repositories, where multiple interconnected tables (relations) are
involved. These methods search for relevant features both from a target
relation (in which each tuple is associated with a class label) and relations
related to the target, in order to better classify target relation tuples.
However, in many practical database applications, such as credit card fraud
detection and disease diagnosis, the target tuples are highly imbalanced. That
is, the number of examples of one class (majority class) in the target relation
is much higher than the others (minority classes). Many existing methods thus
tend to produce poor predictive performance over the underrepresented class in
the data. This paper presents a strategy to deal with such imbalanced
multirelational data. The method learns from multiple views (feature sets) of
relational data in order to construct view learners with different awareness of
the imbalanced problem. These different observations possessed by multiple view
learners are then combined, in order to yield a model which has better
knowledge on both the majority and minority classes in a relational database.
Experiments performed on six benchmarking data sets show that the proposed
method achieves promising results when compared with other popular relational
data mining algorithms, in terms of the ROC curve and AUC value obtained. In
particular, an important result indicates that the method is superior when the
class imbalanced is very high.
