  This research explores Cost-Sensitive Learning (CSL) in the fraud detection
domain to decrease the fraud class's incorrect predictions and increase its
accuracy. Notably, we concentrate on shill bidding fraud that is challenging to
detect because the behavior of shill and legitimate bidders are similar. We
investigate CSL within the Semi-Supervised Classification (SSC) framework to
address the scarcity of labeled fraud data. Our paper is the first attempt to
integrate CSL with SSC for fraud detection. We adopt a meta-CSL approach to
manage the costs of misclassification errors, while SSC algorithms are trained
with imbalanced data. Using an actual shill bidding dataset, we assess the
performance of several hybrid models of CSL and SSC and then compare their
misclassification error and accuracy rates statistically. The most efficient
CSL+SSC model was able to detect 99% of fraudsters and with the lowest total
cost.
