Efficient Progressive Sampling

Efficient Progressive Sampling (1999)

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by Foster Provost , David Jensen , Tim Oates

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Datum	Value	Source
TITLE	Efficient Progressive Sampling	user correction - Legacy Corrections
AUTHOR NAME	Foster Provost	user correction
AUTHOR AFFIL	Bell Atlantic Science and Technology	user correction
AUTHOR ADDR	500 Westchester Avenue; White Plains, New York 10604	user correction
AUTHOR NAME	David Jensen	user correction
AUTHOR AFFIL	Computer Science Department; University of Massachusetts	user correction
AUTHOR ADDR	Amherst, MA 01003-4610	user correction
AUTHOR NAME	Tim Oates	user correction
AUTHOR AFFIL	Computer Science Department; University of Massachusetts	user correction
AUTHOR ADDR	Amherst, MA 01003-4610	user correction
ABSTRACT	Having access to massiveamounts of data does not necessarily imply that induction algorithms must use them all. Samples often provide the same accuracy with far less computational cost. However, the correct sample size is rarely obvious. We analyze methods for progressive sampling---starting with small samples and progressively increasing them as long as model accuracy improves. We show that a simple, geometric sampling schedule is efficient in an asymptotic sense. We then explore the notion of optimal efficiency: what is the absolute best sampling schedule? We describe the issues involved in instantiating an "optimally efficient" progressive sampler. Finally,we provide empirical results comparing a variety of progressive sampling methods. We conclude that progressive sampling often is preferable to analyzing all data instances.	user correction - Legacy Corrections
YEAR	1999	user correction - Legacy Corrections
CITATIONS	13 found	ParsCit 1.0