Technical Note: Bias and the Quantification of Stability

Download
  1. (PDF, 199 KB)
  2. Get@NRC: Technical Note: Bias and the Quantification of Stability (Opens in a new window)
AuthorSearch for:
TypeArticle
Journal titleJournal of Machine Learning
Volume20
Subjectstability; bias; accuracy; repeatability; agreement; similarity
AbstractResearch on bias in machine learning algorithms has generally been concerned with the impact of bias on predictive accuracy. We believe that there are other factors that should also play a role in the evaluation of bias. One such factor is the stability of the algorithm; in other words, the repeatability of the results. If we obtain two sets of data from the same phenomenon, with the same underlying probability distribution, then we would like our learning algorithm to induce approximately the same concepts from both sets of data. This paper introduces a method for quantifying stability, based on a measure of the agreement between concepts. We also discuss the relationships among stability, predictive accuracy, and bias.
Publication date
LanguageEnglish
AffiliationNRC Institute for Information Technology; National Research Council Canada
Peer reviewedNo
NRC number38313
NPARC number5763905
Export citationExport as RIS
Report a correctionReport a correction
Record identifier65ee50c5-df90-4b21-ba99-481600f0d37c
Record created2009-03-29
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)