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IEEE/ICACT20260202 Slide.07        [Big Slide]	Oral Presentation
	Isolation forest is being utilized in a variety of fields due to its high scalability and suitability for large-scale training data. It is based on the idea that anomalies can be more easily separated from normal data points in the feature space. Following this concept, it operates in three main steps: generating random trees, measuring path lengths, and calculating anomaly scores.

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