title: Technical note: Bias and the quantification of stability
creator: Turney, Peter D.
subject: Artificial Intelligence
subject: Machine Learning
subject: Statistical Models
description: Research 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.
publisher: Kluwer
date: 1995
type: Journal (Paginated)
type: PeerReviewed
format: application/pdf
identifier: http://cogprints.org/1819/3/NRC-38313.pdf
identifier:   Turney, Peter D.  (1995) Technical note: Bias and the quantification of stability.  [Journal (Paginated)]     
relation: http://cogprints.org/1819/