New methods for the analysis of binarized BIOLOG GN data of Vibrio species: Minimization of stochastic complexity and cumulative classification

Mats Gyllenberg; Timo Koski; Peter Dawyndt; Tatu Lund; Fabiano Thompson; Brian Austin; Jean Swings

New methods for the analysis of binarized BIOLOG GN data of Vibrio species: Minimization of stochastic complexity and cumulative classification

Mats Gyllenberg, Timo Koski, Peter Dawyndt, Tatu Lund, Fabiano Thompson, Brian Austin, Jean Swings

Research output: Contribution to journal › Article › peer-review

Abstract

We apply minimization of stochastic complexity and the closely related method of cumulative classification to analyse the extensively studied BIOLOG GN data of Vibrio spp. Minimization of stochastic complexity provides an objective tool of bacterial taxonomy as it produces classifications that are optimal from the point of view of information theory. We compare the outcome of our results with previously published classifications of the same data set. Our results both confirm earlier detected relationships between species and discover new ones.

Original language	English
Pages (from-to)	403-415
Number of pages	13
Journal	Systematic and Applied Microbiology
Volume	25
Issue number	3
Publication status	Published - Oct 2002

Keywords

Bacterial taxonomy
Cumulative classification
Machine learning

Cite this

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abstract = "We apply minimization of stochastic complexity and the closely related method of cumulative classification to analyse the extensively studied BIOLOG GN data of Vibrio spp. Minimization of stochastic complexity provides an objective tool of bacterial taxonomy as it produces classifications that are optimal from the point of view of information theory. We compare the outcome of our results with previously published classifications of the same data set. Our results both confirm earlier detected relationships between species and discover new ones.",

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AU - Dawyndt, Peter

AU - Lund, Tatu

AU - Thompson, Fabiano

AU - Austin, Brian

AU - Swings, Jean

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AB - We apply minimization of stochastic complexity and the closely related method of cumulative classification to analyse the extensively studied BIOLOG GN data of Vibrio spp. Minimization of stochastic complexity provides an objective tool of bacterial taxonomy as it produces classifications that are optimal from the point of view of information theory. We compare the outcome of our results with previously published classifications of the same data set. Our results both confirm earlier detected relationships between species and discover new ones.

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KW - Cumulative classification

KW - Machine learning

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New methods for the analysis of binarized BIOLOG GN data of Vibrio species: Minimization of stochastic complexity and cumulative classification

Abstract

Keywords

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