Is Active Learning Green? An Empirical Study

IEEE International Conference on Big Data (BigData), pp. 3823–3829

Abstract

Active learning (AL) is a machine learning (ML) approach that entails carefully choosing the most informative samples for annotation during training, aiming to minimize annotation costs. AL has recently emerged as a promising approach in the context of green ML, as an energy-efficient learning method on top of being data-efficient. Nevertheless, given the significant cost of AL, it might lead one to question the effectiveness of this approach in reducing computational costs and promoting green ML. In this paper, we conduct a comparative analysis of both fundamental and advanced active learning methods against a random baseline selection, aiming to demonstrate the efficacy of active learning to reduce the cost of training. This study demonstrates that, with careful tuning of hyperparameters like query size and pool size, AL is able to reduce runtime while maintaining competitive accuracy for classification tasks.

Authors 2

1. Shirin Salehi Aachen Chair of Information Theory and Data Analytics

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Information Theory and Data Analytics (INDA),Aachen,Germany

   Chair of Information Theory and Data Analytics (INDA), RWTH Aachen University, Aachen, Germany

2. Anke Schmeink Aachen Chair of Information Theory and Data Analytics

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Information Theory and Data Analytics (INDA),Aachen,Germany

   Chair of Information Theory and Data Analytics (INDA), RWTH Aachen University, Aachen, Germany