Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Empirical Study on Feature Discretization   When dealing with continuous numeric features, we usually adopt feature
discretization. In this work, to find the best way to conduct feature
discretization, we present some theoretical analysis, in which we focus on
analyzing correctness and robustness of feature discretization. Then, we
propose a novel discretization method called Local Linear Encoding (LLE).
Experiments on two numeric datasets show that, LLE can outperform conventional
discretization method with much fewer model parameters.

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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: econ
3
The categories of this paper are: physics
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The categories of this paper are: solv-int
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th