Ensemble learning approaches

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

As mentioned in Chap. 1, ensemble learning is helpful to improve overall accuracy of classification. This chapter introduces three approaches of ensemble learning namely, parallel learning, sequential learning and hybrid learning. In particular, some popular methods for ensemble learning, such as Bagging and Boosting, are illustrated in detail. These methods are also discussed comparatively with respects to their advantages and disadvantages.

Original languageEnglish
Title of host publicationRule Based Systems for Big Data
Subtitle of host publicationA Machine Learning Approach
PublisherSpringer
Pages63-73
Number of pages11
Edition1st
ISBN (Electronic)9783319236964
ISBN (Print)9783319236957, 9783319370279
DOIs
Publication statusPublished - 17 Sept 2015

Publication series

NameStudies in Big Data
Volume13
ISSN (Print)2197-6503
ISSN (Electronic)2197-6511

Keywords

  • ensemble learning approach
  • equal voting
  • random forest
  • rule learning algorithm
  • rule-based learning method

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