Heterogeneous versus Homogeneous Machine Learning Ensembles

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@Article{5080,

• author = "Aleksandra Petrakova and Michael Affenzeller and Galina Merkuryeva",
• title = "Heterogeneous versus Homogeneous Machine Learning Ensembles",
• journal = "Information Technology and Management Science",
• year = "2015",
• volume = "18",
• number = "1",
• pages = "135--140",
• month = dec,
• keywords = "genetic algorithms, genetic programming, Classification task, ensemble modeling, machine learning, majority voting",
• ISSN = "2255-9094",
• URL = "https://www.researchgate.net/profile/Michael_Affenzeller2/publication/293194221_Heterogeneous_versus_Homogeneous_Machine_Learning_Ensembles/links/56f031ba08ae70bdd6c9453a/Heterogeneous-versus-Homogeneous-Machine-Learning-Ensembles.pdf",
• broken = "https://content.sciendo.com/view/journals/itms/18/1/article-p135.xml",
• DOI = "doi:10.1515/itms-2015-0021",
• size = "6 pages",
• abstract = "The research demonstrates efficiency of the heterogeneous model ensemble application for a cancer diagnostic procedure. Machine learning methods used for the ensemble model training are neural networks, random forest, support vector machine and offspring selection genetic algorithm. Training of models and the ensemble design is performed by means of HeuristicLab software. The data used in the research have been provided by the General Hospital of Linz, Austria.",

}

Genetic Programming entries for Aleksandra Petrakova Michael Affenzeller Galina Merkurjeva

Citations