Evolution of Space-Partitioning Forest for Anomaly Detection.
GPTP(2017)
Abstract
Previous work proposed a fast one-class anomaly detector using an ensemble of random half-space partitioning trees. The method was shown to be effective and efficient for detecting anomalies in streaming data. However, the parameters were pre-defined, so the random partitions of the data space might not be optimal. Therefore, the aims of this study were to: (a) give some mathematical analysis of the random partitioning trees; and (b) explore optimizing forests for anomaly detection using evolutionary algorithms.
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