Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Optimal Classification Trees: Strong Max-Flow Formulations   We consider the problem of learning optimal binary classification trees.
Literature on the topic has burgeoned in recent years, motivated both by the
empirical suboptimality of heuristic approaches and the tremendous improvements
in mixed-integer programming (MIP) technology. Yet, existing approaches from
the literature do not leverage the power of MIP to its full extent. Indeed,
they rely on weak formulations, resulting in slow convergence and large
optimality gaps. To fill this gap in the literature, we propose a flow-based
MIP formulation for optimal binary classification trees that has a stronger
linear programming relaxation. Our formulation presents an attractive
decomposable structure. We exploit this structure and max-flow/min-cut duality
to derive a Benders' decomposition method, which scales to larger instances. We
conduct extensive computational experiments on standard benchmark datasets on
which we show that our proposed approaches are 50 times faster than
state-of-the art MIP-based techniques and improve out of sample performance up
to 13.8%.

20
The categories of this paper are: acc-phys
37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ