Then, twin bounded support vector machine (TBSVM) is used to get two clustering hyperplanes such that each hyperplane is closer to data points of one group and as far as possible from the data points of other group. Based on these hyperplanes, each non-leaf node is splitted to generate the decision tree.
Apr 1, 2020
Then, twin bounded support vector machine (TBSVM) is used to get two clustering hyperplanes such that each hyperplane is closer to data points of one group and ...
The proposed heterogeneous oblique double RaF employs several linear classifiers at each non-leaf node on the bootstrapped data and splits the original data ...
Then, twin bounded support vector machine (TBSVM) is used to get two clustering hyperplanes such that each hyperplane is closer to data points of one group and ...
Then, twin bounded support vector machine (TBSVM) is used to get two clustering hyperplanes such that each hyperplane is closer to data points of one group and ...
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A novel approach is proposed to generate the oblique decision tree ensemble via TBSVM. • Structural risk minimization principle is implemented in the proposed ...
The oblique ensembles of double random forest models are multivariate decision trees. At each non-leaf node, multisurface proximal support vector machine ...
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We propose a new oblique decision tree algorithm based on support vector machines. Our algorithm produces a single model for a multi-class target variable.