Sampling Unknown Decision Functions to Build Classifier Copies
This work addresses the need for efficient sampling in building classifier copies, but it appears incremental as it focuses on improving existing methods.
The paper tackled the problem of generating unlabeled point sets to explore a classifier's decision behavior for copying, proposing two sampling strategies and validating them on six problems with comparisons to standard methods in terms of accuracy and computational cost.
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.