Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Decision Tree as Local Interpretable Model in Autoencoder-based
  LIME   Nowadays, deep neural networks are being used in many domains because of
their high accuracy results. However, they are considered as "black box", means
that they are not explainable for humans. On the other hand, in some tasks such
as medical, economic, and self-driving cars, users want the model to be
interpretable to decide if they can trust these results or not. In this work,
we present a modified version of an autoencoder-based approach for local
interpretability called ALIME. The ALIME itself is inspired by a famous method
called Local Interpretable Model-agnostic Explanations (LIME). LIME generates a
single instance level explanation by generating new data around the instance
and training a local linear interpretable model. ALIME uses an autoencoder to
weigh the new data around the sample. Nevertheless, the ALIME uses a linear
model as the interpretable model to be trained locally, just like the LIME.
This work proposes a new approach, which uses a decision tree instead of the
linear model, as the interpretable model. We evaluate the proposed model in
case of stability, local fidelity, and interpretability on different datasets.
Compared to ALIME, the experiments show significant results on stability and
local fidelity and improved results on interpretability.

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The categories of this paper are: cond-mat
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The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
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The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
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The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: chem-ph
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The categories of this paper are: math
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The categories of this paper are: nucl-th
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The categories of this paper are: q-alg
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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org