多目标回归旨在使用一组共同的输入变量来预测多个连续变量,其现有方法可归类为问题转换法和算法适应法.它的主要挑战在于如何对输入与输出空间的复杂关系进行建模,以及如何有效利用目标间的相关性.然而,现有的问题转换法很少同时考虑...多目标回归旨在使用一组共同的输入变量来预测多个连续变量,其现有方法可归类为问题转换法和算法适应法.它的主要挑战在于如何对输入与输出空间的复杂关系进行建模,以及如何有效利用目标间的相关性.然而,现有的问题转换法很少同时考虑到这两方面.基于此,本文构建了一种问题转换法同时应对这两大挑战,提出了一种结合目标特定特征和目标相关性的多目标回归方法(Multi-Target Regression via Specific Features and Inter-Target Correlations,TSF-TC).TSF-TC通过对分箱后的样本进行聚类分析构建目标特定特征从而对输入与输出空间的复杂关系进行建模,通过有选择性地堆叠单目标预测值揭示目标间的相关性.本文使用TSF-TC在18个多目标回归数据集上与现有多目标回归方法进行了对比实验,实验结果充分表明了TSF-TC的优势.展开更多
Rope shovels are used to dig and load materials in surface mines. One of the main factors that influence the production rate and energy consumption of rope shovels is the performance of the operator. This paper presen...Rope shovels are used to dig and load materials in surface mines. One of the main factors that influence the production rate and energy consumption of rope shovels is the performance of the operator. This paper presents a method for evaluating rope shovel operators using the Multi-Attribute Decision-Making （MADM） model. Data used in this research were collected from an operating surface coal mine in the southern United States. The MADM model consists of attributes, their weights of importance, and alter- natives. Shovel operators are considered the alternatives, The energy consumption model was developed with multiple regression analysis, and its variables were included in the MADM model as attributes. Preferences with respect to min/max of the defined attributes were obtained with multi-objective opti- mization. Multi-objective optimization was conducted with the overall goal of minimizing energy con- sumption and maximizing production rate. Weights of importance of the attributes were determined by the Analytical Hierarchy Process （AHP）, The overall evaluation of operators was performed by one of the MADM models, i.e., PROMETHEE If. The research results presented here may be used by mining professionals to held evaluate the performance of rode shovel operators in surface mining.展开更多
文摘多目标回归旨在使用一组共同的输入变量来预测多个连续变量,其现有方法可归类为问题转换法和算法适应法.它的主要挑战在于如何对输入与输出空间的复杂关系进行建模,以及如何有效利用目标间的相关性.然而,现有的问题转换法很少同时考虑到这两方面.基于此,本文构建了一种问题转换法同时应对这两大挑战,提出了一种结合目标特定特征和目标相关性的多目标回归方法(Multi-Target Regression via Specific Features and Inter-Target Correlations,TSF-TC).TSF-TC通过对分箱后的样本进行聚类分析构建目标特定特征从而对输入与输出空间的复杂关系进行建模,通过有选择性地堆叠单目标预测值揭示目标间的相关性.本文使用TSF-TC在18个多目标回归数据集上与现有多目标回归方法进行了对比实验,实验结果充分表明了TSF-TC的优势.
文摘Rope shovels are used to dig and load materials in surface mines. One of the main factors that influence the production rate and energy consumption of rope shovels is the performance of the operator. This paper presents a method for evaluating rope shovel operators using the Multi-Attribute Decision-Making （MADM） model. Data used in this research were collected from an operating surface coal mine in the southern United States. The MADM model consists of attributes, their weights of importance, and alter- natives. Shovel operators are considered the alternatives, The energy consumption model was developed with multiple regression analysis, and its variables were included in the MADM model as attributes. Preferences with respect to min/max of the defined attributes were obtained with multi-objective opti- mization. Multi-objective optimization was conducted with the overall goal of minimizing energy con- sumption and maximizing production rate. Weights of importance of the attributes were determined by the Analytical Hierarchy Process （AHP）, The overall evaluation of operators was performed by one of the MADM models, i.e., PROMETHEE If. The research results presented here may be used by mining professionals to held evaluate the performance of rode shovel operators in surface mining.
