Frugal Machine Learning

TU/e Research Portal

Abstract

Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet of Things. In most machine learning applications, the main focus is on the quality of the results achieved (e.g., prediction accuracy), and hence vast amounts of data are being collected, requiring significant computational resources to build models. In many scenarios, however, it is infeasible or impractical to set up large centralized data repositories. In personal health, for instance, privacy issues may inhibit the sharing of detailed personal data. In such cases, machine learning should ideally be performed on wearable devices themselves, which raises major computational limitations such as the battery capacity of smartwatches. This paper thus investigates frugal learning, aimed to build the most accurate possible models using the least amount of resources. A wide range of learning algorithms is examined through a frugal lens, analyzing their accuracy/runtime performance on a wide range of data sets. The most promising algorithms are thereafter assessed in a real-world scenario by implementing them in a smartwatch and letting them learn activity recognition models on the watch itself.

Authors 5

1. Mikhail Evchenko
2. Joaquin Vanschoren

   Eindhoven University of Technology

   Affiliation as printed

   Eindhoven University of Technology

3. Holger H. Hoos Aachen

   Leiden University

   Affiliation as printed

   Leiden University

4. Marc Schoenauer

   Institut national de recherche en sciences et technologies du numérique

   Affiliation as printed

   French Institute for Research in Computer Science and Automation

5. Michèle Sébag

   Université Paris-Sud

   Affiliation as printed

   Laboratory for Computer Science (University of Paris-Sud)