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- W2897826612 abstract "To simultaneously perform model selection and hyperparameter optimization without human intervention, a hierarchical problem by nature, is the aim of the expanding area of Automated Machine Learning (AutoML). Although many search methods have been applied to approach this problem, swarm intelligence algorithms, largely used in model selection tasks with low computational cost, have not yet been considered in a hierarchical manner. Therefore, this work proposes Hierarchical Ant Colony Model Builder (HACO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>MB</sub> ), a modification of the Ant Colony Optimization for mixed-variables (ACO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>MV</sub> ) algorithm that enables it to tackle the hierarchical search space of the AutoML problem for classification tasks. The proposed method was evaluated in the job of selecting SVM kernels and optimizing its hyperparameters. Results of HACO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>MB</sub> on a suite of benchmark datasets show that the method is much faster, while as accurate as or better when compared to a naive approach to the problem." @default.
- W2897826612 created "2018-10-26" @default.
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- W2897826612 date "2018-07-01" @default.
- W2897826612 modified "2023-10-16" @default.
- W2897826612 title "Hierarchical Ant Colony for Simultaneous Classifier Selection and Hyperparameter Optimization" @default.
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- W2897826612 doi "https://doi.org/10.1109/cec.2018.8477834" @default.
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