Bayesian Example Selection Using BaBiES - ScholarlyCommons

University of Pennsylvania

ScholarlyCommons Technical Reports (CIS)

Department of Computer & Information Science

January 2004

Bayesian Example Selection Using BaBiES Andrew I. Schein University of Pennsylvania

S. Ted Sandler University of Pennsylvania

Lyle H. Ungar University of Pennsylvania, [email protected]

Follow this and additional works at: http://repository.upenn.edu/cis_reports Recommended Citation Andrew I. Schein, S. Ted Sandler, and Lyle H. Ungar, "Bayesian Example Selection Using BaBiES", . January 2004.

University of Pennsylvania Department of Computer and Information Science Technical Report No. MS-CIS-04-08. This paper is posted at ScholarlyCommons. http://repository.upenn.edu/cis_reports/10 For more information, please contact [email protected].

Bayesian Example Selection Using BaBiES Abstract

Active learning is widely used to select which examples from a pool should be labeled to give best results when learning predictive models. It is, however, sometimes desirable to choose examples before any labeling or machine learning has occurred. The optimal experimental design literature has many theoretically attractive optimality criteria for example selection, but most are intractable when working with large numbers of predictive features. We present the BaBiES criterion, an approximation of Bayesian A-optimal design for linear regression using binary predictors, which is both simple and extremely fast. Empirical evaluations demonstrate that, in spite of selecting all examples prior to learning, BaBiES is competitive with standard active learning methods for a variety of document classification tasks. Comments

University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-04-08.

This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/10

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1

0.9

0.9

0.8

0.8

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Accuracy

Accuracy

0.7

0.6

0.6

Random BaBiES Uncertainty Sampling Query by Committee Entire Pool Length

0.5 Random BaBiES Uncertainty Sampling Query by Committee Entire Pool

0.5

0.4

0.4

0.3

0.3 50

100

150

200

250 300 350 Number of Articles Labeled

400

450

500

50

1

100

150

200


400

450

500

1

0.95

0.95

0.9 0.9

Accuracy

Accuracy

0.85 0.85

0.8

0.8


0.75

0.7

0.65

0.65

0.6 50

100

150

200 250 Number of Web Pages Labeled

300

350

400

50

0.98

1

0.96

0.98

0.94

0.96

0.92

0.94 Accuracy

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0.7

0.9

0.88

100

150

200 250 Number of Web Pages Labeled

0.84

400



350

0.92

0.9

0.86

300

0.86

0.82

0.84 0

20

40

60


140

160

180

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200

0

20

40

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140

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