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Citations per year

Number of citations per year for the bioinformatics software tool Weka

Tool usage distribution map

This map represents all the scientific publications referring to Weka per scientific context
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Weka specifications


Unique identifier OMICS_23788
Name Weka
Alternative name Waikato Environment for Knowledge Analysis
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
License BSD 3-clause “New” or “Revised” License
Computer skills Advanced
Version 3.8
Stability Stable
Maintained Yes




No version available


  • person_outline Mark Hall
  • person_outline Eibe Frank
  • person_outline Geoffrey Holmes
  • person_outline Bernhard Pfahringer
  • person_outline Peter Reutemann

Publications for Waikato Environment for Knowledge Analysis

Weka citations


Transcriptome Wide Annotation of m5C RNA Modifications Using Machine Learning

Front Plant Sci
PMCID: 5915569
PMID: 29720995
DOI: 10.3389/fpls.2018.00519

[…] ootstrapped samples and features. The output of the RF-based m5C prediction model was determined by a majority vote of the classification trees. The RF algorithm was implemented using the R package “Rweka” (Hornik et al., ), which provides an R environment to invoke the ML package “weka” (v3.9.1; https://www.cs.waikato.ac.nz/ml/weka). […]


Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project

PLoS One
PMCID: 5905952
PMID: 29668729
DOI: 10.1371/journal.pone.0195344

[…] a straight line which separates those points into 2 types and is situated as far as possible from all those points. Training the SVM is done using Sequential Minimal Optimization algorithm []. We use Weka implementation of SMO []. We test SVM using polynomial, normalized polynomial, puk kernels and vary the complexity parameter {0.1, 10, and 30}. The value of the complexity parameter controls the […]


Accelerated discovery of metallic glasses through iteration of machine learning and high throughput experiments

Sci Adv
PMCID: 5898831
PMID: 29662953
DOI: 10.1126/sciadv.aaq1566

[…] : one for alloys that contain only metallic elements and the other alloys that contain at least one nonmetal or metalloid. Each ML model was trained using a random forest algorithm, as implemented in Weka (, ). Full details of the model and a web interface for using it for arbitrary compositions are available at oqmd.org/static/analytics/composition.html.The sputtering GFA model was trained using […]


Comparing the performance of meta classifiers—a case study on selected imbalanced data sets relevant for prediction of liver toxicity

J Comput Aided Mol Des
PMCID: 5919997
PMID: 29626291
DOI: 10.1007/s10822-018-0116-z

[…] ques; (2) algorithm-oriented methods; and (3) combinatorial/ensemble/hybrid techniques [, , , , ].Several studies compared classifiers that handle imbalanced datasets. Schierz et al. [] compared four WEKA classifiers (Naïve Bayes, SVM, Random Forest and J48 tree) and reported SVM and J48 to be the best performing for bioassay datasets. Lin and Chen in 2013 found SVM threshold adjustment as the bes […]


Epigenetic alterations are associated with monocyte immune dysfunctions in HIV 1 infection

Sci Rep
PMCID: 5882962
PMID: 29615725
DOI: 10.1038/s41598-018-23841-1

[…] d using customized functions available from Bioconductor packages. After dataset analysis, a decision tree was generated for each heat map. The C4.5 algorithm was used to build the decision tree from WEKA implementation software (Waikato Environment for Knowledge Analysis, version 3.6.11, University of Waikato, New Zealand), using default J48 parameters. The decision trees, the most widely used ma […]


Double Windows Based Motion Recognition in Multi Floor Buildings Assisted by a Built In Barometer

PMCID: 5948638
PMID: 29614791
DOI: 10.3390/s18041061

[…] machine learning algorithms, including Random Forest (RF), J48 Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machines (SVM) and Naive Bayes (NB). They are all implemented by the Weka toolkit [] developed by Professor Witten [], the university of Waikato, New Zealand. The 10-folds cross-validation is adopted, and the recognition accuracy of each multi-floor motion is compared […]

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Weka institution(s)
Pentaho Corporation, Orlando, FL, USA; Department of Computer Science, University of Waikato, Hamilton, New Zealand

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