| 引用本文: | 高虹雷,门昌骞,王文剑.多核贝叶斯优化的模型决策树算法.[J].国防科技大学学报,2022,44(3):67-76.[点击复制] |
| GAO Honglei,MEN Changqian,WANG Wenjian.Algorithm for model decision tree with multi-kernel Bayesian optimization[J].Journal of National University of Defense Technology,2022,44(3):67-76[点击复制] |
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| 多核贝叶斯优化的模型决策树算法 |
| 高虹雷1,门昌骞1,王文剑1,2 |
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(1. 山西大学 计算机与信息技术学院, 山西 太原 030006;2. 山西大学 计算智能与中文信息处理教育部重点实验室, 山西 太原 030006)
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| 摘要: |
| 构造模型决策树时超参数较多,参数组合复杂,利用网格搜索等调参方法将会消耗大量的时间,影响模型性能的提升。提出了一种多核贝叶斯优化的模型决策树算法,该算法为应对不同分类数据特性,采用三种高斯过程建模寻优,利用贝叶斯优化技术,选出最优的参数组合。实验结果表明,所提算法在参数寻优上要优于传统的模型决策树寻优方法,并且能够在迭代次数不多的情况下找到全局最优参数值,在一定程度上提升了算法的分类性能,节省了大量的调参时间。 |
| 关键词: 模型决策树 贝叶斯优化 高斯过程 |
| DOI：10.11887/j.cn.202203009 |
| 投稿日期：2020-06-08 |
| 基金项目:国家自然科学基金资助项目(62076154,U1805263)；中央引导地方科技发展资金资助项目(YDZX20201400001224)；山西省国际科技合作重点研发计划资助项目(201903D421050)；山西省自然科学基金资助项目(201901D111030) |
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| Algorithm for model decision tree with multi-kernel Bayesian optimization |
| GAO Honglei1, MEN Changqian1, WANG Wenjian1,2 |
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(1. School of Computer and Information Technology, Shanxi University, Taiyuan 030006, China;2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, China)
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| Abstract: |
| In the construction of the model decision tree, there are many parameters and the parameter combination is complex. The use of grid search and other parameter tuning methods will consume a lot of time, which will affect the improvement of the model performance. A model decision tree with multi-kernel bayesian optimization was proposed. In order to deal with the characteristics of different classified data, three Gaussian processes were used for modeling optimization. The Bayesian optimization technique was used to select the best parameter combination. The experimental results show that the proposed algorithm is better than the traditional model decision tree method in parameter optimization, and can find the global optimal parameter value in the case of a few iterations. To a certain extent, it improves the classification performance of the algorithm and saves a lot of parameter adjustment time. |
| Keywords: model decision tree Bayesian optimization Gaussian process |
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