Sampling Unknown Decision Functions to Build Classifier Copies

TL;DR

This paper introduces two novel sampling strategies to generate unlabeled data for effectively copying classifiers, validated across multiple problems with a focus on accuracy and computational efficiency.

Contribution

It proposes new sampling methods for exploring classifier decision boundaries, improving copying effectiveness over standard approaches.

Findings

The proposed sampling strategies outperform standard methods in accuracy.

The methods are computationally efficient across tested problems.

Validated on six well-known classification tasks.

Abstract

Copies have been proposed as a viable alternative to endow machine learning models with properties and features that adapt them to changing needs. A fundamental step of the copying process is generating an unlabelled set of points to explore the decision behavior of the targeted classifier throughout the input space. In this article we propose two sampling strategies to produce such sets. We validate them in six well-known problems and compare them with two standard methods. We evaluate our proposals in terms of both their accuracy performance and their computational cost.