@misc{cogprints1819,
volume = {20},
title = {Technical note: Bias and the quantification of stability},
author = {Peter D. Turney},
publisher = {Kluwer},
year = {1995},
pages = {23--33},
journal = {Machine Learning},
keywords = {stability, bias, accuracy, repeatability, agreement, similarity.},
url = {http://cogprints.org/1819/},
abstract = {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.}
}