A comparative assessment of machine learning algorithms for events detection

Abstract

Nowadays, one can observe massive amount of data production by numerous devices interacting with their environment and end users. [1] Such data can be the subject of advanced processing usually through machine learning algorithms. Hence, we are able to provide intelligent applications and analytics in many research domains like health informatics, information technology, environmental sciences, and so on so forth. However, choosing the appropriate machine learning model for data processing can be one of the most difficult tasks. In this paper, we try to facilitate researchers providing a 'benchmarking' of multiple machine learning algorithms to reveal their advantages and drawbacks. This effort mostly focuses on the accuracy of the studied algorithms and adopts various datasets found in the respective literature. We provide a short description of the adopted models, the datasets and extensive experimental evaluation accompanied by numerical results and our qualitative review on the outcomes. © 2019 IEEE.