Optimal Mixed Integer Linear Optimization Trained Multivariate Classification Trees

TL;DR

This paper introduces two novel MILO formulations for designing optimal multivariate classification trees that balance accuracy and complexity, demonstrating improved scalability and robustness over existing methods.

Contribution

The paper proposes two new cut-based MILO models for optimal binary classification trees, improving upon existing formulations in scalability and theoretical strength.

Findings

Models outperform traditional approaches in scalability.

Demonstrated robustness in out-of-sample tests.

Theoretical improvements over existing MILO formulations.

Abstract

Multivariate decision trees are powerful machine learning tools for classification and regression that attract many researchers and industry professionals. An optimal binary tree has two types of vertices, (i) branching vertices which have exactly two children and where datapoints are assessed on a set of discrete features and (ii) leaf vertices at which datapoints are given a prediction, and can be obtained by solving a biobjective optimization problem that seeks to (i) maximize the number of correctly classified datapoints and (ii) minimize the number of branching vertices. Branching vertices are linear combinations of training features and therefore can be thought of as hyperplanes. In this paper, we propose two cut-based mixed integer linear optimization (MILO) formulations for designing optimal binary classification trees (leaf vertices assign discrete classes). Our models leverage on-the-fly identification of minimal infeasible subsystems (MISs) from which we derive cutting planes that hold the form of packing constraints. We show theoretical improvements on the strongest flow-based MILO formulation currently in the literature and conduct experiments on publicly available datasets to show our models' ability to scale, strength against traditional branch and bound approaches, and robustness in out-of-sample test performance. Our code and data are available on GitHub.