Machine Learning in the Wild: The Case of User-Centered Learning in Cyber Physical Systems

Atieh R. Khamesi, Eura Shin, Simone Silvestri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

Smart environments, such as smart cities and smart homes, are Cyber-Physical-Systems (CPSs) which are becoming an increasing part of our everyday lives. Several applications in these systems, such as energy management through home appliance identification or activity recognition, adopt Machine Learning (ML) as a practical tool for extracting useful knowledge from raw data. These applications are usually characterized by a sequential stream of data, unlike the classical ML scenario in which the entire data is available during training. For such applications, Stream-based Active Learning (SAL) has been designed as a type of supervised ML in which an expert is asked to label the most informative instances as they arrive. Previous SAL techniques assume that the expert is always available and always labels the data correctly. However, in several applications, such as those mentioned above, the SAL activity interweaves with the everyday life of regular residents, who are often not experts, and may also not always be willing to participate in the labeling process. In this paper, we discuss the importance of user-centered ML, and show how taking into account realistic models of user behavior significantly improves the accuracy and reduces the training period of smart environment applications based on SAL. We consider two use cases, namely appliance identification and activity recognition. Results based on real data sets show an improvement in terms of accuracy up to 55.38%.

Original languageEnglish
Title of host publication2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020
Pages275-281
Number of pages7
ISBN (Electronic)9781728131870
DOIs
StatePublished - Jan 2020
Event2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020 - Bengaluru, India
Duration: Jan 7 2020Jan 11 2020

Publication series

Name2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020

Conference

Conference2020 International Conference on COMmunication Systems and NETworkS, COMSNETS 2020
Country/TerritoryIndia
CityBengaluru
Period1/7/201/11/20

Bibliographical note

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Active Learning
  • Cyber-Physical-Systems (CPSs)
  • Internet of Things (IoT)
  • Machine Learning
  • Smart City
  • Smart Home
  • User-Centered

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Signal Processing

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