Comparing Anomaly Detection and Classification Algorithms: A Case Study in Two Domains

• Utilizing large data sets in practical scenarios usually requires identifying, annotating and classifying rare events or anomalies. Although several methods exists, there are two classes of algorithms: anomaly detection algorithms and classification algorithms. Both types of algorithms have different characteristics and in this paper, we set out to compare them on two cases. We use data from a neurointensive care unit and from microwave radio transmissions. We apply Isolation Forest and Random Forest algorithms to find events in the data that occur with a frequency of ca. 1%. The results show that classification algorithms (Random Forest) perform better and can achieve up to 100% accuracy, while the anomaly detection algorithms (Isolation Forest) can achieve only 73% at best. Based on the results, we conclude that it is better to invest in annotating data á priori and use classification algorithms, despite the lower costs of using the anomaly detection algorithms.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

neuro-intensive care

Machine learning

telecommunication

Publikations- och innehållstyp

kon (ämneskategori)

ref (ämneskategori)