OASIS: An Active Framework for Set Inversion

TL;DR

OASIS is an active learning framework using SVMs designed to efficiently solve high-dimensional set inversion problems with fewer data points, outperforming existing methods like VISIA.

Contribution

We introduce OASIS, a novel active learning approach employing SVMs for set inversion, demonstrating improved efficiency and scalability in high-dimensional nonlinear models.

Findings

OASIS outperforms VISIA in simulation studies.

The method maintains robustness as dimensionality increases.

OASIS reduces computational cost compared to traditional methods.

Abstract

In this work, we introduce a novel method for solving the set inversion problem by formulating it as a binary classification problem. Aiming to develop a fast algorithm that can work effectively with high-dimensional and computationally expensive nonlinear models, we focus on active learning, a family of new and powerful techniques which can achieve the same level of accuracy with fewer data points compared to traditional learning methods. Specifically, we propose OASIS, an active learning framework using Support Vector Machine algorithms for solving the problem of set inversion. Our method works well in high dimensions and its computational cost is relatively robust to the increase of dimension. We illustrate the performance of OASIS by several simulation studies and show that our algorithm outperforms VISIA, the state-of-the-art method.