The detection of anomalies is a research area that has made great progressin recent years and decades. As more and more applications produce everlarger amounts of data, anomaly detection becomes...Show moreThe detection of anomalies is a research area that has made great progressin recent years and decades. As more and more applications produce everlarger amounts of data, anomaly detection becomes increasingly important.In the past most anomaly detection algorithms focused on static data sets,that is data sets with not time stamp or element, and did not take the ele-ment of time into account if it was provided. In addition, these algorithmsrarely have the ability to incorporate additional knowledge into their decision-making process and cannot adapt to changes in the data over time. Buildingon an algorithm called Evolutionary Isolation Forest which attempts to solveboth of these problems, this paper suggests a variation of this algorithm calledExtended Evolutionary Isolation Forest. This algorithm uses more complexsplitting criteria to isolate anomalies and uses evolutionary operators to refinethe decision process and adapt to feedback from experts. Using benchmarkdata, it can be shown that the algorithm performs similarly to the Evolution-ary Isolation Forest, but without generally outperforming it. In addition, thealgorithms are compared with a real-world data set from the energy infras-tructure provided by WithTheGrid.Show less
