Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Model-free feature selection to facilitate automatic discovery of
  divergent subgroups in tabular data   Data-centric AI encourages the need of cleaning and understanding of data in
order to achieve trustworthy AI. Existing technologies, such as AutoML, make it
easier to design and train models automatically, but there is a lack of a
similar level of capabilities to extract data-centric insights. Manual
stratification of tabular data per a feature (e.g., gender) is limited to scale
up for higher feature dimension, which could be addressed using automatic
discovery of divergent subgroups. Nonetheless, these automatic discovery
techniques often search across potentially exponential combinations of features
that could be simplified using a preceding feature selection step. Existing
feature selection techniques for tabular data often involve fitting a
particular model in order to select important features. However, such
model-based selection is prone to model-bias and spurious correlations in
addition to requiring extra resource to design, fine-tune and train a model. In
this paper, we propose a model-free and sparsity-based automatic feature
selection (SAFS) framework to facilitate automatic discovery of divergent
subgroups. Different from filter-based selection techniques, we exploit the
sparsity of objective measures among feature values to rank and select
features. We validated SAFS across two publicly available datasets (MIMIC-III
and Allstate Claims) and compared it with six existing feature selection
methods. SAFS achieves a reduction of feature selection time by a factor of 81x
and 104x, averaged cross the existing methods in the MIMIC-III and Claims
datasets respectively. SAFS-selected features are also shown to achieve
competitive detection performance, e.g., 18.3% of features selected by SAFS in
the Claims dataset detected divergent samples similar to those detected by
using the whole features with a Jaccard similarity of 0.95 but with a 16x
reduction in detection time.

17
The categories of this paper are: q-fin
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn