Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty
  Calibration   Most machine learning classifiers only concern classification accuracy, while
certain applications (such as medical diagnosis, meteorological forecasting,
and computation advertising) require the model to predict the true probability,
known as a calibrated estimate. In previous work, researchers have developed
several calibration methods to post-process the outputs of a predictor to
obtain calibrated values, such as binning and scaling methods. Compared with
scaling, binning methods are shown to have distribution-free theoretical
guarantees, which motivates us to prefer binning methods for calibration.
However, we notice that existing binning methods have several drawbacks: (a)
the binning scheme only considers the original prediction values, thus limiting
the calibration performance; and (b) the binning approach is non-individual,
mapping multiple samples in a bin to the same value, and thus is not suitable
for order-sensitive applications. In this paper, we propose a feature-aware
binning framework, called Multiple Boosting Calibration Trees (MBCT), along
with a multi-view calibration loss to tackle the above issues. Our MBCT
optimizes the binning scheme by the tree structures of features, and adopts a
linear function in a tree node to achieve individual calibration. Our MBCT is
non-monotonic, and has the potential to improve order accuracy, due to its
learnable binning scheme and the individual calibration. We conduct
comprehensive experiments on three datasets in different fields. Results show
that our method outperforms all competing models in terms of both calibration
error and order accuracy. We also conduct simulation experiments, justifying
that the proposed multi-view calibration loss is a better metric in modeling
calibration error.

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The categories of this paper are: eess
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: stat
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The categories of this paper are: bayes-an
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The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org