Practical Active Learning with Model Selection for Small Data

TL;DR

This paper presents a practical active learning method with model selection tailored for small datasets, focusing on binary classification with support vector machines, and demonstrates its effectiveness in low-label scenarios.

Contribution

It introduces a fast, simple active learning approach with model selection for very small datasets, addressing a gap in practical applications with limited labeling budgets.

Findings

The method finds hyperparameters that outperform an oracle on difficult datasets.

It achieves reasonable performance on easier datasets.

Weighted model selection can be tuned based on domain knowledge.

Abstract

Active learning is of great interest for many practical applications, especially in industry and the physical sciences, where there is a strong need to minimize the number of costly experiments necessary to train predictive models. However, there remain significant challenges for the adoption of active learning methods in many practical applications. One important challenge is that many methods assume a fixed model, where model hyperparameters are chosen a priori. In practice, it is rarely true that a good model will be known in advance. Existing methods for active learning with model selection typically depend on a medium-sized labeling budget. In this work, we focus on the case of having a very small labeling budget, on the order of a few dozen data points, and develop a simple and fast method for practical active learning with model selection. Our method is based on an underlying pool-based active learner for binary classification using support vector classification with a radial basis function kernel. First we show empirically that our method is able to find hyperparameters that lead to the best performance compared to an oracle model on less separable, difficult to classify datasets, and reasonable performance on datasets that are more separable and easier to classify. Then, we demonstrate that it is possible to refine our model selection method using a weighted approach to trade-off between achieving optimal performance on datasets that are easy to classify, versus datasets that are difficult to classify, which can be tuned based on prior domain knowledge about the dataset.