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- W4226143810 abstract "Decision trees are widely-used classification and regression models because of their interpretability and good accuracy. Classical methods such as CART are based on greedy approaches but a growing attention has recently been devoted to optimal decision trees. We investigate the nonlinear continuous optimization formulation proposed in Blanquero et al. (EJOR, vol. 284, 2020; COR, vol. 132, 2021) for (sparse) optimal randomized classification trees. Sparsity is important not only for feature selection but also to improve interpretability. We first consider alternative methods to sparsify such trees based on concave approximations of the $l_{0}$ ``norm. Promising results are obtained on 24 datasets in comparison with $l_1$ and $l_{infty}$ regularizations. Then, we derive bounds on the VC dimension of multivariate randomized classification trees. Finally, since training is computationally challenging for large datasets, we propose a general decomposition scheme and an efficient version of it. Experiments on larger datasets show that the proposed decomposition method is able to significantly reduce the training times without compromising the accuracy." @default.
- W4226143810 created "2022-05-05" @default.
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- W4226143810 date "2021-12-09" @default.
- W4226143810 modified "2023-09-26" @default.
- W4226143810 title "On multivariate randomized classification trees: $l_0$-based sparsity, VC~dimension and decomposition methods" @default.
- W4226143810 hasPublicationYear "2021" @default.
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