Active learning for electrodermal activity classification
Name
Picard_Active learning.pdf
Size
272.46 KB
Format
Adobe PDF
Checksum (MD5)
f2a171cc843dd3151c053dd397801f44
Author(s)
Xia, Victoria F.
Jaques, Natasha Mary
Taylor, Sara Ann
Fedor, Szymon
Picard, Rosalind W.
Date Issued
February 2016
Journal
2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Xia, Victoria, Natasha Jaques, Sara Taylor, Szymon Fedor, and Rosalind Picard. “Active Learning for Electrodermal Activity Classification.” 2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) (December 2015).
Version
Author's final manuscript
Abstract
To filter noise or detect features within physiological signals, it is often effective to encode expert knowledge into a model such as a machine learning classifier. However, training such a model can require much effort on the part of the researcher; this often takes the form of manually labeling portions of signal needed to represent the concept being trained. Active learning is a technique for reducing human effort by developing a classifier that can intelligently select the most relevant data samples and ask for labels for only those samples, in an iterative process. In this paper we demonstrate that active learning can reduce the labeling effort required of researchers by as much as 84% for our application, while offering equivalent or even slightly improved machine learning performance.
MIT Department
Massachusetts Institute of Technology. Media Laboratory. Affective Computing Group
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Media Laboratory
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/SPMB.2015.7405467
