Hub
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Hub
  • Nodes
  • A2DE (3.7)
NodeNode / Learner

A2DE (3.7)

Analytics Integrations Weka Weka (3.7) Classification Algorithms
+2
Drag & drop
Like
Copy short link

A2DE achieves highly accurate classification by averaging over all of a small space of alternative naive-Bayes-like models that have weaker (and hence less detrimental) independence assumptions than naive Bayes

The resulting algorithm is computationally efficient while delivering highly accurate classification on many learning tasks.

For more information, see

Webb, Geoffrey I., Boughton, Janice, Zheng, Fei, Ting, Kai Ming, Salem, Houssam (2012).Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive {Bayesian} classification.

Machine Learning.86(2):233-272.

Further papers are available at

http://www.csse.monash.edu.au/~webb/.

Use m-estimate for smoothing base probability estimates witha default of 1 (m value can changed via option -M).Default mode is non-incremental that is probabilites are computed at learning time.

An incremental version can be used via option -I.

Default frequency limit set to 1.

(based on WEKA 3.7)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.

Node details

Input ports
  1. Type: Table
    Training data
    Training data
Output ports
  1. Type: Weka 3.7 Classifier
    Trained model
    Trained model

Extension

The A2DE (3.7) node is part of this extension:

  1. Go to item

Related workflows & nodes

    1. Go to item

    KNIME
    Open for Innovation

    KNIME AG
    Hardturmstrasse 66
    8005 Zurich, Switzerland
    • Software
    • Getting started
    • Documentation
    • E-Learning course
    • Solutions
    • KNIME Hub
    • KNIME Forum
    • Blog
    • Events
    • Partner
    • Developers
    • KNIME Home
    • KNIME Open Source Story
    • Careers
    • Contact us
    Download KNIME Analytics Platform Read more on KNIME Server
    © 2022 KNIME AG. All rights reserved.
    • Trademarks
    • Imprint
    • Privacy
    • Terms & Conditions
    • Credits