基于粗糙集的不确定信息知识发现及在城市交通管理中的应用研究
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摘要
随着社会经济的快速发展、城市规模的不断扩大以及城市人口和机动车保有量的不断增加,交通拥堵现象日趋严重,交通拥挤正成为一个普遍的社会现象。尤其是在大城市,交通拥挤问题已经成为影响城市功能发挥和城市可持续发展的关键。由于现代城市交通管理是高新技术的综合集成,任何一个微小的差错都可能隐藏着重大的交通安全隐患,并可能导致交通事故,造成重大损失。因此,信息化是城市交通管理的未来发展方向,即应用最新的科学技术,尤其是信息技术实现对交通流运行状态的实时感知、实时预测和实时监控,而数据挖掘与知识发现是其中的一项重要内容。如何有效地管理与处理海量的交通管理数据,以实现对城市交通的信息化管理和控制是管理人员、科研人员及工程技术人员目前面临的一个重大问题。
粗糙集作为一种新的处理不确定知识的数学工具,与其他知识发现技术相比具有独特优势,它无需提供问题所需处理的数据集合之外的任何先验信息,通过知识约简与知识依赖性分析,从中发现隐含的信息和知识,揭示潜在的规律。因此,粗糙集理论可以有效地帮助决策者处理复杂系统的决策问题。本文主要研究了基于粗糙集的不确定信息处理与知识发现及决策过程框架,在此框架基础上,研究了粗糙集在不确定信息知识发现和决策支持领域的应用理论,包括连续数据离散化、属性约简、知识发现、推理和解释、以及应用粗糙集方法进行城市交通管理中的不确定信息知识发现和决策的问题。
本文的主要研究成果如下：
引入集群智能优化理论和粗糙集理论,提出基于改进粒子群优化的粗糙集连续属性离散化算法。采用5组典型的UCI数据集breas、iris、wine、glass、heart对提出的改进粒子群优化的离散化算方法同基于属性重要性、基于信息熵及遗传算法进行对比实验,结果表明本文提出的离散化算法能使决策系统的信息损失降低到最小,并可获得简洁的决策规则。
针对最小属性约简这个NP问题,推广了分辨矩阵的概念,从信息论角度,构建相对分辨矩阵,以条件信息熵为启发式信息,提出一种新型的启发式属性约简算法,并从理论上对该算法进行分析,实例结果表明,该算法能有效地对决策信息系统进行属性约简；针对大规模数据集的决策信息系统,本章引入求异矩阵的概念,并在此基础上提出直接求取最小相对约简的基于属性区分频度的约简改进算法,该算法能够将更多的启发式信息融合起来,将重复的逻辑运算转化为求异矩阵运算,理论分析及实例结果都验证了它优于其他一般的属性约简算法,更能获取最小相对约简集。
提出两种信息系统的规则获取算法。针对完备信息系统,提出了一种全局寻优的粗糙集决策规则获取算法,该算法从连续属性离散化开始,基于核属性,每次增加一个最重要的属性,直至获取最优属性约简集,最后基于核的值约简算法获取所有确定的或可能的决策规则集,并给出规则的评价指标支持度和确定度的计算方法；针对不完备信息系统,引入相容属性矩阵和决策属性矩阵的概念,提出基于相容关系的不完备信息系统规则获取的矩阵算法,该算法无需计算核属性,能够直接从决策系统中提取所有规则集,提高了算法效率,为大规模数据集的规则获取提供了一种新的思路。算法分析和实例结果均表明上述两种算法的可行性和有效性。
关于城市交通管理决策问题,本文以粗糙集理论为工具,针对城市交通流状态模式识别问题进行了深入研究。分析了城市交通管理中的不确定性因素,介绍了交通状态模式识别的一般过程和方法,提出了交通流状态模式分类粗糙集知识发现模型及算法,融入集成策略构建了交通流状态模式集成分类系统,并给出交通流运行状态的模式识别算法。实例验证了将粗糙集理论应用于城市交通流状态模式分类知识发现与决策具有较大的应用价值。
论文的研究成果对城市交通的信息化管理和决策提供了一定的理论依据和实践指导。
With the rapid development of social economy, continuous expansion of city size, continuous increasing of urban population and vehicles, traffic congestion phenomenon are more and more serious. Traffic congestion is becoming a common social phenomenon. Especially in large cities, traffic congestion has become a key of urban to feature play and sustainable development. As modern urban traffic management is a comprehensive integrated high-tech, any small error could hide significant traffic safety hazard and may cause traffic accidents, result in significant losses. Therefore, information is the future direction of urban traffic management, which apply the latest science and technology, especially information technology to achieve real-time operating state of traffic flow awareness, real-time forecasts and real-time monitoring, and data mining and knowledge discovery is the most important contents. How to manage and process vast amounts of traffic management datas effectively to achieve information management and control on urban traffic is currently a major problem of manager, scientific researcher and engineering staff.
Rough sets as a new mathematical tool to deal with uncertain knowledge, compared with others knowledge discovery technology, Rough sets have unique advantages, it need not provide any prior information and knowledge which required processing data collection, through analysis of knowledge reduction and dependency, hidden information or knowledge are discovered and potential rules are revealed. Therefore, rough sets theory can help decision-makers deal with complex systems. This dissertation studied knowledge discovery and decision process framework from uncertain information based on rough sets. Under the basic framework, application theories of knowledge discovery from uncertain information and decision support fields based on rough sets are studied, including discretization, attribute reduction, knowledge discovery, reasoning and interpretation, knowledge discovery from uncertain information and decision-making on urban traffic management based on rough sets theory.
The main results of this dissertation are as follows:
Through introduction of swarm intelligence optimization theory and rough sets theroy, an improved particle swarm optimization algorithm of continuous attribute descretization is presented. A typical5-group UCI data sets such as breast, iris, wine, glass and heart are adopted to improved particle swarm optimization algorithms. compared with discretization algorithms such as attribute significance based, information entropy and genetic algorithm, the experiment results show that improved discretization algorithm can make minimize loss of information and access to the simplest decision rules from decision-making systems.
Considering the minimum attribute reduction NP problem, the concept of discernibility matrix is promoted in this dissertation, and relative discernibility matrix is constructed from perspective of information theory, as conditional entropy to heuristic information, a novel heuristic attribute reduction algorithm is presented, the theoretical analysis and example results show that the algorithm can reduct decision-making information system effectively; Considering large data sets for decision information system, this dissertation introduce concepts of divergent matrix, based on this concept, an improved attribute reduction algorithm which calculate a minimum relative reduction directly based on attribute frequency is constructed, the reduction algorithm can fuse more heuristic information, it will convet repeated divergent logic operation into matrix operation, theoretical analysis and example results demonstrate that this algorithm is superior to other general attribute reduction algorithm, and get minimum relative reduction sets better.
Two decision-making rules acquisition algorithms are presented. For complete information system, a global optimizied algorithm for rough sets decision-making rules is presented, the algorithm begin to continuous attributes discretization, based on core attributes, the most important attribute is increased to get the best attribute reduction sets, and finally, all determined and possible decision-making rule sets based on core attribute value reduciton algorithm are acquisizied, calculate methods on evaluation index of certainty and support are given; For incomplete information system, this dissertation introduce concepts of condition attribute matrix and decision attribute matrix, an matrix decision-making rule algorithm based on compatibility relationship from incomplete informaiton system are presented, the algorithm can extract all rules from decision-making system directly without calculating core attributes, so improve algorithm efficiency, which provide a new way for large-scale data sets. Theoretical analysis and example results show that two algorithms are effective.
To urban traffic management decision-making issues, this dissertation as rough sets theory a tool, pattern recognition on urban traffic state issues is deeply studied. This dissertation analysized uncertainty factors on urban traffic management, studied the general process and methods of pattern recognition on urban traffic flow state, and calssification knowledge discovery models and algorithms on traffic flow state pattern recognition are peresented, an integrated classification system on traffic flow state pattern from integrated strategy perspective is constructed, and traffic flow state pattern recognition algorithm is presented. Example results show that rough sets theory applied to urban traffic flow state classification knowledge discovery and decision-making have significant application value.
Research achievements provide a theoretical basis and practical guidance to urban traffic information management and decision-making.
粗糙集作为一种新的处理不确定知识的数学工具,与其他知识发现技术相比具有独特优势,它无需提供问题所需处理的数据集合之外的任何先验信息,通过知识约简与知识依赖性分析,从中发现隐含的信息和知识,揭示潜在的规律。因此,粗糙集理论可以有效地帮助决策者处理复杂系统的决策问题。本文主要研究了基于粗糙集的不确定信息处理与知识发现及决策过程框架,在此框架基础上,研究了粗糙集在不确定信息知识发现和决策支持领域的应用理论,包括连续数据离散化、属性约简、知识发现、推理和解释、以及应用粗糙集方法进行城市交通管理中的不确定信息知识发现和决策的问题。
本文的主要研究成果如下：
引入集群智能优化理论和粗糙集理论,提出基于改进粒子群优化的粗糙集连续属性离散化算法。采用5组典型的UCI数据集breas、iris、wine、glass、heart对提出的改进粒子群优化的离散化算方法同基于属性重要性、基于信息熵及遗传算法进行对比实验,结果表明本文提出的离散化算法能使决策系统的信息损失降低到最小,并可获得简洁的决策规则。
针对最小属性约简这个NP问题,推广了分辨矩阵的概念,从信息论角度,构建相对分辨矩阵,以条件信息熵为启发式信息,提出一种新型的启发式属性约简算法,并从理论上对该算法进行分析,实例结果表明,该算法能有效地对决策信息系统进行属性约简；针对大规模数据集的决策信息系统,本章引入求异矩阵的概念,并在此基础上提出直接求取最小相对约简的基于属性区分频度的约简改进算法,该算法能够将更多的启发式信息融合起来,将重复的逻辑运算转化为求异矩阵运算,理论分析及实例结果都验证了它优于其他一般的属性约简算法,更能获取最小相对约简集。
提出两种信息系统的规则获取算法。针对完备信息系统,提出了一种全局寻优的粗糙集决策规则获取算法,该算法从连续属性离散化开始,基于核属性,每次增加一个最重要的属性,直至获取最优属性约简集,最后基于核的值约简算法获取所有确定的或可能的决策规则集,并给出规则的评价指标支持度和确定度的计算方法；针对不完备信息系统,引入相容属性矩阵和决策属性矩阵的概念,提出基于相容关系的不完备信息系统规则获取的矩阵算法,该算法无需计算核属性,能够直接从决策系统中提取所有规则集,提高了算法效率,为大规模数据集的规则获取提供了一种新的思路。算法分析和实例结果均表明上述两种算法的可行性和有效性。
关于城市交通管理决策问题,本文以粗糙集理论为工具,针对城市交通流状态模式识别问题进行了深入研究。分析了城市交通管理中的不确定性因素,介绍了交通状态模式识别的一般过程和方法,提出了交通流状态模式分类粗糙集知识发现模型及算法,融入集成策略构建了交通流状态模式集成分类系统,并给出交通流运行状态的模式识别算法。实例验证了将粗糙集理论应用于城市交通流状态模式分类知识发现与决策具有较大的应用价值。
论文的研究成果对城市交通的信息化管理和决策提供了一定的理论依据和实践指导。
With the rapid development of social economy, continuous expansion of city size, continuous increasing of urban population and vehicles, traffic congestion phenomenon are more and more serious. Traffic congestion is becoming a common social phenomenon. Especially in large cities, traffic congestion has become a key of urban to feature play and sustainable development. As modern urban traffic management is a comprehensive integrated high-tech, any small error could hide significant traffic safety hazard and may cause traffic accidents, result in significant losses. Therefore, information is the future direction of urban traffic management, which apply the latest science and technology, especially information technology to achieve real-time operating state of traffic flow awareness, real-time forecasts and real-time monitoring, and data mining and knowledge discovery is the most important contents. How to manage and process vast amounts of traffic management datas effectively to achieve information management and control on urban traffic is currently a major problem of manager, scientific researcher and engineering staff.
Rough sets as a new mathematical tool to deal with uncertain knowledge, compared with others knowledge discovery technology, Rough sets have unique advantages, it need not provide any prior information and knowledge which required processing data collection, through analysis of knowledge reduction and dependency, hidden information or knowledge are discovered and potential rules are revealed. Therefore, rough sets theory can help decision-makers deal with complex systems. This dissertation studied knowledge discovery and decision process framework from uncertain information based on rough sets. Under the basic framework, application theories of knowledge discovery from uncertain information and decision support fields based on rough sets are studied, including discretization, attribute reduction, knowledge discovery, reasoning and interpretation, knowledge discovery from uncertain information and decision-making on urban traffic management based on rough sets theory.
The main results of this dissertation are as follows:
Through introduction of swarm intelligence optimization theory and rough sets theroy, an improved particle swarm optimization algorithm of continuous attribute descretization is presented. A typical5-group UCI data sets such as breast, iris, wine, glass and heart are adopted to improved particle swarm optimization algorithms. compared with discretization algorithms such as attribute significance based, information entropy and genetic algorithm, the experiment results show that improved discretization algorithm can make minimize loss of information and access to the simplest decision rules from decision-making systems.
Considering the minimum attribute reduction NP problem, the concept of discernibility matrix is promoted in this dissertation, and relative discernibility matrix is constructed from perspective of information theory, as conditional entropy to heuristic information, a novel heuristic attribute reduction algorithm is presented, the theoretical analysis and example results show that the algorithm can reduct decision-making information system effectively; Considering large data sets for decision information system, this dissertation introduce concepts of divergent matrix, based on this concept, an improved attribute reduction algorithm which calculate a minimum relative reduction directly based on attribute frequency is constructed, the reduction algorithm can fuse more heuristic information, it will convet repeated divergent logic operation into matrix operation, theoretical analysis and example results demonstrate that this algorithm is superior to other general attribute reduction algorithm, and get minimum relative reduction sets better.
Two decision-making rules acquisition algorithms are presented. For complete information system, a global optimizied algorithm for rough sets decision-making rules is presented, the algorithm begin to continuous attributes discretization, based on core attributes, the most important attribute is increased to get the best attribute reduction sets, and finally, all determined and possible decision-making rule sets based on core attribute value reduciton algorithm are acquisizied, calculate methods on evaluation index of certainty and support are given; For incomplete information system, this dissertation introduce concepts of condition attribute matrix and decision attribute matrix, an matrix decision-making rule algorithm based on compatibility relationship from incomplete informaiton system are presented, the algorithm can extract all rules from decision-making system directly without calculating core attributes, so improve algorithm efficiency, which provide a new way for large-scale data sets. Theoretical analysis and example results show that two algorithms are effective.
To urban traffic management decision-making issues, this dissertation as rough sets theory a tool, pattern recognition on urban traffic state issues is deeply studied. This dissertation analysized uncertainty factors on urban traffic management, studied the general process and methods of pattern recognition on urban traffic flow state, and calssification knowledge discovery models and algorithms on traffic flow state pattern recognition are peresented, an integrated classification system on traffic flow state pattern from integrated strategy perspective is constructed, and traffic flow state pattern recognition algorithm is presented. Example results show that rough sets theory applied to urban traffic flow state classification knowledge discovery and decision-making have significant application value.
Research achievements provide a theoretical basis and practical guidance to urban traffic information management and decision-making.
引文
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