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Genetic Architecture and Sample Size Impact Relative Performance of Nonlinear Machine Learning and Standard Polygenic Risk Scores

Zhu J, Baousi A, Morris AP, Guo H.
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The random subspace method for constructing decision forests

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998
Much of previous attention on decision trees focuses on the splitting criteria and optimization of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom resolved. A method to construct a decision tree based classifier is proposed that maintains highest accuracy on training data and improves on generalization accuracy as ...
Tin Kam Ho
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Interpretation of QSAR Models Based on Random Forest Methods

Molecular Informatics, 2011
AbstractA new algorithm for the interpretation of Random Forest models has been developed. It allows to calculate the contribution of each descriptor to the calculated property value. In case of the simplex representation of a molecular structure, contributions of individual atoms can be calculated, and thus it becomes possible to estimate the ...
Victor E, Kuz'min, Pavel G, Polishchuk, Anatoly G, Artemenko, Sergey A, Andronati   +3 more
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Combination methods in a Fuzzy Random Forest

2008 IEEE International Conference on Systems, Man and Cybernetics, 2008
When individual classifiers are combined appropriately, we usually obtain a better performance in terms of classification precision. Multi-classifiers are the result of combining several individual classifiers. In this work we propose and compare various combination methods to obtain the final decision of the multi-classifier based on a ...
Piero P. Bonissone, José Manuel Cadenas, María del Carmen Garrido, Ramon Andrés Díaz-Valladares   +3 more
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