Abstract
Ensemble learning refers to the simultaneous use of multiple learning algorithms for the purpose of a better predictive power. Using SPSS Modeler a 250 patient data file with 28 variables, mainly patients’ gene expression levels, were analyzed with linear regression, generalized linear model, nearest neighbor clustering, support vector machines, decision trees, chi-squares models, and neural networks. The correlation coefficients of the three best models were used for computing an average score and its errors. This average score was a lot better than those of the separate algorithms.
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In this chapter SPSS modeler was used. It is a software program entirely distinct from SPSS statistical software, though it uses most if not all of the calculus methods of it. It is a standard software package particularly used by market analysts, but, as shown, can, perfectly, well be applied for exploratory purposes in medical research. Alternatively, R statistical software, Knime (Konstanz information miner machine learning software), the packages for Support Vector Machines (LIBSVM), and ensembled support vector machines (ESVM), and many more software programs can be used for ensembled analyses.
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Cleophas, T.J., Zwinderman, A.H. (2017). Ensembled Correlation Coefficients. In: Modern Meta-Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-55895-0_17
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DOI: https://doi.org/10.1007/978-3-319-55895-0_17
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