Conical picks are important tools for rock mechanical excavation.Mean cutting force(MCF)of conical pick determines the suitability of the target rock for mechanical excavation.Accurate evaluation of MCF is important f...Conical picks are important tools for rock mechanical excavation.Mean cutting force(MCF)of conical pick determines the suitability of the target rock for mechanical excavation.Accurate evaluation of MCF is important for pick design and rock cutting.This study proposed hybrid methods composed of boosting trees and Bayesian optimization(BO)for accurate evaluation of MCF.220 datasets including uniaxial compression strength,tensile strength,tip angle(θ),attack angle,and cutting depth,were collected.Four boosting trees were developed based on the database to predict MCF.BO optimized the hyper-parameters of these boosting trees.Model evaluation suggested that the proposed hybrid models outperformed many commonly utilized machine learning models.The hybrid model composed of BO and categorical boosting(BO-CatBoost)was the best.Its outstanding performance was attributed to its advantages in dealing with categorical features(θincluded 6 types of angles and could be considered as categorical features).A graphical user interface was developed to facilitate the application of BO-CatBoost for the estimation of MCF.Moreover,the influences of the input parameters on the model and their relationship with MCF were analyzed.Whenθincreased from 80°to 90°,it had a significant contribution to the increase of MCF.展开更多
针对代价敏感学习问题,研究boosting算法的代价敏感扩展。提出一种基于代价敏感采样的代价敏感boosting学习方法,通过在原始boosting每轮迭代中引入代价敏感采样,最小化代价敏感损失期望。基于上述学习框架,推导出两种代价敏感boosting...针对代价敏感学习问题,研究boosting算法的代价敏感扩展。提出一种基于代价敏感采样的代价敏感boosting学习方法,通过在原始boosting每轮迭代中引入代价敏感采样,最小化代价敏感损失期望。基于上述学习框架,推导出两种代价敏感boosting算法,同时,揭示并解释已有算法的不稳定本质。在加州大学欧文分校(University of California,Irvine,UCI)数据集和麻省理工学院生物和计算学习中心(Center for Biological&Computational Learning,CBCL)人脸数据集上的实验结果表明,对于代价敏感分类问题,代价敏感采样boosting算法优于原始boosting和已有代价敏感boosting算法。展开更多
Boosting is one of the most representational ensemble prediction methods. It can be divided into two se-ries: Boost-by-majority and Adaboost. This paper briefly introduces the research status of Boosting and one of it...Boosting is one of the most representational ensemble prediction methods. It can be divided into two se-ries: Boost-by-majority and Adaboost. This paper briefly introduces the research status of Boosting and one of its seri-als-AdaBoost,analyzes the typical algorithms of AdaBoost.展开更多
基金Project(52374153)supported by the National Natural Science Foundation of ChinaProject(2023zzts0726)supported by the Fundamental Research Funds for the Central Universities of Central South University,China。
文摘Conical picks are important tools for rock mechanical excavation.Mean cutting force(MCF)of conical pick determines the suitability of the target rock for mechanical excavation.Accurate evaluation of MCF is important for pick design and rock cutting.This study proposed hybrid methods composed of boosting trees and Bayesian optimization(BO)for accurate evaluation of MCF.220 datasets including uniaxial compression strength,tensile strength,tip angle(θ),attack angle,and cutting depth,were collected.Four boosting trees were developed based on the database to predict MCF.BO optimized the hyper-parameters of these boosting trees.Model evaluation suggested that the proposed hybrid models outperformed many commonly utilized machine learning models.The hybrid model composed of BO and categorical boosting(BO-CatBoost)was the best.Its outstanding performance was attributed to its advantages in dealing with categorical features(θincluded 6 types of angles and could be considered as categorical features).A graphical user interface was developed to facilitate the application of BO-CatBoost for the estimation of MCF.Moreover,the influences of the input parameters on the model and their relationship with MCF were analyzed.Whenθincreased from 80°to 90°,it had a significant contribution to the increase of MCF.
文摘针对代价敏感学习问题,研究boosting算法的代价敏感扩展。提出一种基于代价敏感采样的代价敏感boosting学习方法,通过在原始boosting每轮迭代中引入代价敏感采样,最小化代价敏感损失期望。基于上述学习框架,推导出两种代价敏感boosting算法,同时,揭示并解释已有算法的不稳定本质。在加州大学欧文分校(University of California,Irvine,UCI)数据集和麻省理工学院生物和计算学习中心(Center for Biological&Computational Learning,CBCL)人脸数据集上的实验结果表明,对于代价敏感分类问题,代价敏感采样boosting算法优于原始boosting和已有代价敏感boosting算法。
文摘Boosting is one of the most representational ensemble prediction methods. It can be divided into two se-ries: Boost-by-majority and Adaboost. This paper briefly introduces the research status of Boosting and one of its seri-als-AdaBoost,analyzes the typical algorithms of AdaBoost.
