Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Contrastive Explanations with Local Foil Trees   Recent advances in interpretable Machine Learning (iML) and eXplainable AI
(XAI) construct explanations based on the importance of features in
classification tasks. However, in a high-dimensional feature space this
approach may become unfeasible without restraining the set of important
features. We propose to utilize the human tendency to ask questions like "Why
this output (the fact) instead of that output (the foil)?" to reduce the number
of features to those that play a main role in the asked contrast. Our proposed
method utilizes locally trained one-versus-all decision trees to identify the
disjoint set of rules that causes the tree to classify data points as the foil
and not as the fact. In this study we illustrate this approach on three
benchmark classification tasks.

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The categories of this paper are: nucl-th
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: supr-con
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: alg-geom