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Normalizing feature scores produces erroneous results if unnormalized feature scores sum to negative value #1

@ClemensA4t1qbit

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@ClemensA4t1qbit

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).

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