多维属性离散化能提升机器学习算法训练的速度与精度,目前的离散化算法性能较低且多是单属性离散,忽略了属性之间的潜在关联。基于此,提出了一种基于森林优化的粗糙集离散化算法(a discretization algorithm based on forest optimizati...多维属性离散化能提升机器学习算法训练的速度与精度,目前的离散化算法性能较低且多是单属性离散,忽略了属性之间的潜在关联。基于此,提出了一种基于森林优化的粗糙集离散化算法(a discretization algorithm based on forest optimization and rough set,FORDA)。该算法针对多维连续属性的离散化,依据变精度粗糙集理论,设计适宜值函数,进而构建森林寻优网络,迭代搜索最优断点子集。在UCI数据集上的实验结果表明,与当前主流的离散化算法相比,所提算法能避免局部最优,显著提升了SVM分类器的分类精度,其离散化性能更为优良,且具有一定的通用性,验证了算法的有效性。展开更多
A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional an...A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional and single-line style,a road is no longer a linkage of road nodes but abstracted as a network node.Similarly,a road node is abstracted as the linkage of two ordered single-directional roads.This model can describe turn restrictions,circular roads,and other real scenarios usually described using a super-graph.Then a computing framework for optimal path finding（OPF）is presented.It is proved that classical Dijkstra and A algorithms can be directly used for OPF computing of any real-world road networks by transferring a super-graph to an SLSD network.Finally,using Singapore road network data,the proposed conceptual model and its corresponding optimal path finding algorithms are validated using a two-step optimal path finding algorithm with a pre-computing strategy based on the SLSD road network.展开更多
文摘多维属性离散化能提升机器学习算法训练的速度与精度,目前的离散化算法性能较低且多是单属性离散,忽略了属性之间的潜在关联。基于此,提出了一种基于森林优化的粗糙集离散化算法(a discretization algorithm based on forest optimization and rough set,FORDA)。该算法针对多维连续属性的离散化,依据变精度粗糙集理论,设计适宜值函数,进而构建森林寻优网络,迭代搜索最优断点子集。在UCI数据集上的实验结果表明,与当前主流的离散化算法相比,所提算法能避免局部最优,显著提升了SVM分类器的分类精度,其离散化性能更为优良,且具有一定的通用性,验证了算法的有效性。
基金The National Key Technology R&D Program of China during the 11th Five Year Plan Period(No.2008BAJ11B01)
文摘A solution to compute the optimal path based on a single-line-single-directional（SLSD）road network model is proposed.Unlike the traditional road network model,in the SLSD conceptual model,being single-directional and single-line style,a road is no longer a linkage of road nodes but abstracted as a network node.Similarly,a road node is abstracted as the linkage of two ordered single-directional roads.This model can describe turn restrictions,circular roads,and other real scenarios usually described using a super-graph.Then a computing framework for optimal path finding（OPF）is presented.It is proved that classical Dijkstra and A algorithms can be directly used for OPF computing of any real-world road networks by transferring a super-graph to an SLSD network.Finally,using Singapore road network data,the proposed conceptual model and its corresponding optimal path finding algorithms are validated using a two-step optimal path finding algorithm with a pre-computing strategy based on the SLSD road network.
