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A new modification of Isolation Forest called Attention-Based IsolationForest (ABIForest) for solving the anomaly detection problem is proposed. Itincorporates the attention mechanism in the form of the Nadaraya-Watsonregression into the Isolation Forest for improving solution of the anomalydetection problem. The main idea underlying the modification is to assignattention weights to each path of trees with learnable parameters depending oninstances and trees themselves. The Huber's contamination model is proposed tobe used for defining the attention weights and their parameters. As a result,the attention weights are linearly depend on the learnable attention parameterswhich are trained by solving the standard linear or quadratic optimizationproblem. ABIForest can be viewed as the first modification of Isolation Forest,which incorporates the attention mechanism in a simple way without applyinggradient-based algorithms. Numerical experiments with synthetic and realdatasets illustrate outperforming results of ABIForest. The code of proposedalgorithms is available.

Improved Anomaly Detection by Using the Attention-Based Isolation Forest