Application of machine learning methods for anomaly detection in internet advertising

Abstract

This research deals certain with issues regarding downloading data from the Internet, i.e., Internet page advertising, and certain mechanisms to take care of the integrity of the data that is put into the dedicated processing context afterwards. The work also relates to e-commerce, as some advertising scenarios provide high error rates with pricing, which may be unacceptable in various scenarios, such as renting or selling a home. This paper presents a brief overview of the outlier detection methods and machine learning-based classifiers that are used to determine the number of anomalies in the analyzed dataset. This work contributes to the operation of organizations that deal with data accuracy and integrity, such as home rental or selling agencies.