Cost-minimising strategies for data labelling : optimal stopping and active learning

TL;DR

This paper introduces cost-aware strategies for data labeling in supervised learning, focusing on optimal stopping and active learning to minimize total labeling costs while maintaining model performance.

Contribution

It proposes a cost-based framework for optimal stopping and evaluation in active learning, addressing the trade-off between labeling costs and model accuracy.

Findings

Developed a general optimal stopping strategy based on expected cost.

Provided a framework for empirical evaluation using cost as a performance metric.

Discussed the potential for further research in cost-efficient active learning.

Abstract

Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$ of pairs in $\CX \times \CY$. However, in a lot of applications of interest, acquisition of large amounts of observations is easy, while the process of generating labels is time-consuming or costly. One way to deal with this problem is {\em active} learning, where points to be labelled are selected with the aim of creating a model with better performance than that of an model trained on an equal number of randomly sampled points. In this paper, we instead propose to deal with the labelling cost directly: The learning goal is defined as the minimisation of a cost which is a function of the expected model performance and the total cost of the labels used. This allows the development of general strategies and specific algorithms for (a) optimal stopping, where the expected cost dictates whether label acquisition should continue (b) empirical evaluation, where the cost is used as a performance metric for a given combination of inference, stopping and sampling methods. Though the main focus of the paper is optimal stopping, we also aim to provide the background for further developments and discussion in the related field of active learning.