Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection

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Deep Learning AWS-IF-interpretability
Overview

Why, hello there!

This is the supporting notebook for the research paper — Why Are You Weird? Infusing Interpretability in Isolation Forest for Anomaly Detection — published in the Explainable AI Workshop Proceedings of the 35th AAAI Conference, 2021. Pre-print version is available on arxiv.

Both commented code of the experiments and the results are reproduced at full in the AWS_demo notebook.

Since this is the first release of the implementation of the Assist-Based Weighting Scheme (AWS), which powers our model-specific local explanation method for the Isolation Forest learner, we welcome all constructive feedback and suggestions.

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Comments
  • Normalizing feature scores produces erroneous results if unnormalized feature scores sum to negative value

    Normalizing feature scores produces erroneous results if unnormalized feature scores sum to negative value

    Hi, thank you so much for the very interesting paper and the great code to go with it.

    I have an observation about using the the weighting score as proposed by your paper, in particular the one defined via

    log2(parent / child) - 1

    which leads to some features having negative scores.

    As a result, in some instances, the rowsum of a given outliers' feature scores can be negative as well. Using a negative value to normalize of course flips the sign of the row, so now what used to be the most important feature becomes the least important feature.

    Not sure what the "correct" way to normalize these scores should be. Maybe normalization in such a way is not appropriate in any case, as we want to know which outliers get a "robust" explanation (with overall large feature scores) and which don't (with overall small feature scores).

    opened by ClemensA4t1qbit 2
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