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Open-source machine learning: R meets Weka

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  • Kurt Hornik
  • Christian Buchta
  • Achim Zeileis

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  • Kurt Hornik & Christian Buchta & Achim Zeileis, 2009. "Open-source machine learning: R meets Weka," Computational Statistics, Springer, vol. 24(2), pages 225-232, May.
    • Handle: RePEc:spr:compst:v:24:y:2009:i:2:p:225-232
    DOI: 10.1007/s00180-008-0119-7
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    References listed on IDEAS

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    1. Hahsler, Michael & Grün, Bettina & Hornik, Kurt, 2005. "arules - A Computational Environment for Mining Association Rules and Frequent Item Sets," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i15).
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    3. Ana D. Maldonado & Darío Ramos-López & Pedro A. Aguilera, 2018. "A Comparison of Machine-Learning Methods to Select Socioeconomic Indicators in Cultural Landscapes," Sustainability, MDPI, vol. 10(11), pages 1-16, November.
    4. Hao-Yun Kao & Chi-Chang Chang & Chin-Fang Chang & Ying-Chen Chen & Chalong Cheewakriangkrai & Ya-Ling Tu, 2022. "Associations between Sex and Risk Factors for Predicting Chronic Kidney Disease," IJERPH, MDPI, vol. 19(3), pages 1-11, January.
    5. Gustavo Cattelan Nobre & Elaine Tavares, 2017. "Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 463-492, April.
    6. Grubinger, Thomas & Zeileis, Achim & Pfeiffer, Karl-Peter, 2014. "evtree: Evolutionary Learning of Globally Optimal Classification and Regression Trees in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i01).
    7. Mariane S. Reis & Maria Isabel S. Escada & Luciano V. Dutra & Sidnei J. S. Sant’Anna & Nathan D. Vogt, 2018. "Towards a Reproducible LULC Hierarchical Class Legend for Use in the Southwest of Pará State, Brazil: A Comparison with Remote Sensing Data-Driven Hierarchies," Land, MDPI, vol. 7(2), pages 1-29, May.
    8. Stefano Castellana & Caterina Fusilli & Gianluigi Mazzoccoli & Tommaso Biagini & Daniele Capocefalo & Massimo Carella & Angelo Luigi Vescovi & Tommaso Mazza, 2017. "High-confidence assessment of functional impact of human mitochondrial non-synonymous genome variations by APOGEE," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-12, June.
    9. Simon Urbanek, 2009. "How to talk to strangers: ways to leverage connectivity between R, Java and Objective C," Computational Statistics, Springer, vol. 24(2), pages 303-311, May.
    10. Bogdan Oancea, 2023. "Automatic Product Classification Using Supervised Machine Learning Algorithms in Price Statistics," Mathematics, MDPI, vol. 11(7), pages 1-32, March.
    11. Souhila Ghanem & Raphaël Couturier & Pablo Gregori, 2021. "An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote," Mathematics, MDPI, vol. 9(12), pages 1-12, June.
    12. Chi-Chang Chang & Tse-Hung Huang & Pei-Wei Shueng & Ssu-Han Chen & Chun-Chia Chen & Chi-Jie Lu & Yi-Ju Tseng, 2021. "Developing a Stacked Ensemble-Based Classification Scheme to Predict Second Primary Cancers in Head and Neck Cancer Survivors," IJERPH, MDPI, vol. 18(23), pages 1-10, November.
    13. Maria Janicka & Bogumiła Pawluśkiewicz & Tomasz Gnatowski, 2023. "Preliminary Results of the Introduction of Dicotyledonous Meadow Species," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    14. Rashid Mehmood & Muhammad Riaz & Ronald Does, 2013. "Efficient power computation for r out of m runs rules schemes," Computational Statistics, Springer, vol. 28(2), pages 667-681, April.
    15. Nai-Hua Chen, 2020. "Exploring the Cognitive and Emotional Impact of Online Climate Change Videos on Viewers," Sustainability, MDPI, vol. 12(22), pages 1-16, November.

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