NDLI: Border Sampling through Coupling Markov Chain Monte Carlo

Content Provider	IEEE Xplore Digital Library
Author	Guichong Li Japkowicz, N. Stocki, T.J. Ungar, R.K.
Copyright Year	2008
Description	Author affiliation: Comput. Sci. of Univ. of Ottawa, Ottawa, ON (Guichong Li; Japkowicz, N.) \|\| Radiat. Protection Bur., Health Canada, Ottawa, ON (Stocki, T.J.; Ungar, R.K.)
Abstract	Recently, progressive border sampling (PBS) was proposed for sample selection in supervised learning by progressively learning an augmented full border from small labeled datasets. However, this quadratic learning algorithm is inapplicable to large datasets. In this paper, we incorporate the PBS to a state of the art technique called coupling Markov chain Monte Carlo (CMCMC) in an attempt to scale the original algorithm up on large labeled datasets. The CMCMC can produce an exact sample while a naive strategy for Markov chain Monte Carlo cannot guarantee the convergence to a stationary distribution. The resulting CMCMC-PBS algorithm is thus proposed for border sampling on large datasets. CMCMC-PBS exhibits several remarkable characteristics: linear time complexity, learner-independence, and a consistent convergence to an optimal sample from the original training sets by learning from their subsamples. Our experimental results on the 33 either small or large labeled datasets from the UCIKDD repository and a nuclear security application show that our new approach outperforms many previous sampling techniques for sample selection.
Starting Page	393
Ending Page	402
File Size	398831
Page Count	10
File Format	PDF
ISBN	9780769535029
ISSN	15504786
DOI	10.1109/ICDM.2008.52
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-15
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sampling methods Monte Carlo methods Convergence Costs Data mining Computer science Protection Supervised learning Data security Machine learning markov chain monte carlo sample selection border identification
Content Type	Text
Resource Type	Article

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