The presented K-SIF and SIF algorithms enhance the isolation forest methodology by integrating non-linear properties, crucial for analyzing complex and varied real-world datasets.
Our findings reveal that K-SIF consistently outperforms the traditional FIF in terms of performance, demonstrating the efficacy of our data-driven approach in anomaly detection.
SIF not only achieves leading performance metrics, but it does so with a focus on computational efficiency, making it an effective tool for functional anomaly detection.
The introduction of these novel algorithms significantly reduces the risk of neglecting certain anomaly types, thus accommodating a broader spectrum of data patterns.
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