Efficient Online classification using an Ensemble of Bayesian Linear Logistic Regressors

Narayanan Edakunni, Sethu Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a novel ensemble of logistic linear regressors that combines the robustness of online Bayesian learning with the flexibility of ensembles. The ensemble of classifiers are built on top of a Randomly Varying Coefficient model designed for online regression with the fusion of classifiers done at the level of regression before converting it into a class label using a logistic link function. The locally weighted logistic regressor is compared against the state-of-the-art methods to reveal its excellent generalization performance with low time and space complexities.
Original languageEnglish
Title of host publicationMultiple Classifier Systems
Subtitle of host publication8th International Workshop, MCS 2009, Reykjavik, Iceland, June 10-12, 2009. Proceedings
PublisherSpringer-Verlag GmbH
Pages102-111
Number of pages10
ISBN (Print)978-3-642-02325-5
DOIs
Publication statusPublished - 2009

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin / Heidelberg
Volume5519
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • Informatics
  • Computer Science

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