Abstract:
"
Machine learning has been incorporated into many industries in recent years.
Capturing acts such as committing fraud has become a game-changer with the
integration of machine learning. It is quick as well as efficient and can be used to solve
many real-world problems. Even though machine learning can become an excellent
choice for specific types of problem-solving, it is not always easy to implement. As
the knowledge required to properly handle machine learning is quite extensive, experts
are needed that has the knowledge whether if it's the domain in question or technology.
Click fraud is one of the major issues in the digital advertisement industry. As
advertisements are a factor for businesses to become successful, any form of fraudulent
activity can taint that progress because this will eventually cause them to lose money.
Depending on the different payout systems that are available the type of fraud may
change. But at the end of the day, it can produce a major blow to the industry.
By considering the different factors such as the type of payments or the type of
advertisement, there are solutions developed or being developed. Different learning
models have been tested out with different sets of datasets. In this research, several of
these classifiers like the Logistic Regression, KNN, Lenear Discriminant Analysis,
Quadratic Discriminant Analysis and Extra trees classifier are put to the test for
detecting click fraud where more than 90% performance results are acquired with the
Extra Trees classifier. All the results that were produced are compared with each other
to determine the classifier that has the potential to be further developed into an accurate
fraud detection model."
