Robust Optimal Classification Trees under Noisy Labels
This work addresses the problem of building robust classification trees for practitioners working with potentially noisy labeled datasets, offering an incremental improvement over existing methods.
This paper introduces a new method for constructing Optimal Classification Trees that accounts for noisy labels in training data. It combines SVM-like margin maximization for splitting rules with the ability to correct labels during tree construction to detect noise. The approach is formulated as a Mixed Integer Non Linear Programming problem and tested on standard UCI datasets, demonstrating its effectiveness.
In this paper we propose a novel methodology to construct Optimal Classification Trees that takes into account that noisy labels may occur in the training sample. Our approach rests on two main elements: (1) the splitting rules for the classification trees are designed to maximize the separation margin between classes applying the paradigm of SVM; and (2) some of the labels of the training sample are allowed to be changed during the construction of the tree trying to detect the label noise. Both features are considered and integrated together to design the resulting Optimal Classification Tree. We present a Mixed Integer Non Linear Programming formulation for the problem, suitable to be solved using any of the available off-the-shelf solvers. The model is analyzed and tested on a battery of standard datasets taken from UCI Machine Learning repository, showing the effectiveness of our approach.