Cogprints
Login | Create Account

Technical note: Bias and the quantification of stability

Turney, Peter D. (1995) Technical note: Bias and the quantification of stability. [Journal (Paginated)]

Full text available as:

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
44Kb

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.

Item Type:Journal (Paginated)
Keywords:stability, bias, accuracy, repeatability, agreement, similarity.
Subjects:Computer Science > Artificial Intelligence
Computer Science > Machine Learning
Computer Science > Statistical Models
ID Code:1819
Deposited By:Turney, Peter
Deposited On:13 Oct 2001
Last Modified:12 Sep 2007 17:41

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Carnap, R. (1947). Meaning and necessity: A study in semantics and modal logic.

Chicago: University of Chicago Press.

Famili, A., & Turney, P. (1991). Intelligently helping the human planner in industrial

process planning. Artificial Intelligence for Engineering Design, Analysis and Man-ufacturing,

5, 109-124.

Fraser, D.A.S. (1976). Probability and statistics: Theory and applications. Massachusetts:

Duxbury Press.

Haussler, D. (1988) Quantifying inductive bias: AI learning systems and Valiant’s learning

framework. Artificial Intelligence, 36, 177-221.

Honavar, V. (1992). Inductive learning using generalized distance measures. Proceedings

of the 1992 SPIE Conference on Adaptive and Learning Systems. Orlando, Florida.

Levenshtein, A. (1966). Binary codes capable of correcting deletions, insertions, and

reversals. Soviet Physics, 10, 703-710.

Murphy, P.M. & Pazzani, M.J. (1994). Exploring the decision forest: an empirical investi-gation

of Occam’s razor in decision tree induction. Journal for AI Research, ftp

p.gp.cs.cmu.edu, cd /usr/jair/pub, 1, 257-275.

Quinlan, J.R. (1992). C4.5: Programs for machine learning. California: Morgan

Kaufmann.

Rendell, L. (1986). A general framework for induction and a study of selective induction.

Machine Learning, 1, 177-226.

Schaffer, C. (1992). An empirical technique for quantifying preferential bias in inductive

concept learners. Unpublished manuscript. Department of Computer Science,

CUNY/Hunter College, New York.

Schaffer, C. (1993). Overfitting avoidance as bias. Machine Learning, 10, 153-178.

Utgoff, P.E. (1986). Shift of bias for inductive concept learning. In J.G. Carbonell, R.S.

Michalski, and T.M. Mitchell (eds) Machine Learning: An Artificial Intelligence

Approach, Volume II. California: Morgan Kaufmann.

Vapnik, V.N. (1982). Estimation of dependencies based on empirical data. New York:

Springer.

Metadata

Repository Staff Only: item control page

Cogprints is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.