Machine learning techniques lend themselves as promising decision-making and analytic tools in a wide range of applications. Different ML algorithms have various hyper-parameters. In order to tailor an ML model towards a specific application, a large number of hyper-parameters should be tuned. Tuning the hyper-parameters directly affects the performance (accuracy and run-time). However, for large-scale search spaces, efficiently exploring the ample number of combinations of hyper-parameters is computationally challenging. Existing automated hyper-parameter tuning techniques suffer from high time complexity. In this paper, we propose HyP-ABC, an automatic innovative hybrid hyper-parameter optimization algorithm using the modified artificial bee colony approach, to measure the classification accuracy of three ML algorithms, namely random forest, extreme gradient boosting, and support vector machine. Compared to the state-of-the-art techniques, HyP-ABC is more efficient and has a limited number of parameters to be tuned, making it worthwhile for real-world hyper-parameter optimization problems. We further compare our proposed HyP-ABC algorithm with state-of-the-art techniques. In order to ensure the robustness of the proposed method, the algorithm takes a wide range of feasible hyper-parameter values, and is tested using a real-world educational dataset.
翻译:机械学习技术在广泛的应用中是很有希望的决策和分析工具。不同的ML算法有各种超参数。为了使ML模型适应特定的应用,应该调整大量的超参数。显示超参数直接影响性能(精确度和运行时间)。但是,对于大型搜索空间来说,有效探索大量超参数组合在计算上具有挑战性。现有的自动超参数调法在时间上非常复杂。在本文中,我们提议采用经修改的人工蜂窝法,自动创新的混合超参数优化算法,以测量三种ML算法的分类精度,即随机森林、极端梯度增强和支持矢量机。与最新技术相比,HyP-ABC效率更高,需要调整的参数有限,因此对现实世界超参数的优化问题有价值。我们进一步比较了使用经修改的人工蜂窝法的自动创新混合超参数优化算法,以测量三种ML算法的分类精度,即随机森林、极端梯度增强值和支持矢量机。与最新技术相比,HYP-ABC-ABC具有有限的参数调整价值,我们进一步比较了拟议的HP-ABC系统,用一种可靠的计算方法确保了可靠的真实值的精确度。