核属性蚁群算法的规则获取

蚁群算法是一种新型的模拟进化算法,研究已经表明该算法具有许多优良的性质,并且在优化计算中已得到了很多应用.粗糙集理论作为一种智能数据分析和数据挖掘的新的数学工具,其主要优点在于它不需要任何关于被处理数据的先验或额外知识.本文从规则获取和优化两方面研究基于粗糙集理论和蚁群算法的分类规则挖掘方法.通过研究决策表和决策规则系数,建立基于粗糙集表示和度量的知识理论,将粗糙集理论与蚁群算法融合,采用粗糙集理论进行属性约简,利用蚁群算法获取最优分类规则,优势互补.实验结果比较表明,算法获取的分类规则,具有良好的预测能力和更为简洁的表示形式.     

WBITS中基于神经规则的知识表示与管理

针对当前智能教学系统存在的不足,设计了一个基于WEB的智能教学系统模型。详细介绍了该系统中引入的基于神经规则的知识表示方法并简要说明了推理过程。最后,详细给出了知识获取与知识更新算法,解决了智能教学系统中知识库维护难的问题。     

基于粗糙集的决策规则设计算法研究

从获取确定性决策规则的角度出发,基于粗糙集对高维知识库的决策规则约减知识,采用相关方法设计决策规则实现的算法。在不损失信息量的前提下,使决策规则的表示简单化。使用这种算法生成决策规则的时间大大减少,与白钡窑实际控制的数据相比,决策规则控制的误差小于±1。     

基础信息层经验规则的一种表达方法

该文讨论在复杂的大型辅助决策系统中,构造智能决策规则模型的一种方法。这是一种基于决策表的知识表示方法。它在传统决策表的基础上,吸收了产生式规则、框架表示法、模糊理论、关系模型等多种方法的思想和技术,把传统决策表加以扩展,得到了一种结构性好、表达能力强、可操作性较好的智能决策表达工具,用来表示大型辅助决策系统中的复杂领域知识,将其中松散的经验规则形式化成智能决策规则模型,从而增强其结构性和可操作性,有效支持对其它信息的操作。     

产生式规则专家系统的原理与实现

不确定的知识表示与知识推理是专家系统研究和开发的难点。本文利用“目标驱动”方法中控制模块、规则库和事实数据库的操作原理，使用SQL Server2000和Delphi 6．0作为开发平台，通过数据格式和算法的设计构造并实现了一个产生式规则专家系统。该系统实现了不确定性知识表示和知识推理的计算机化，用户只需要为系统提供足够的已知数据，就可以获得专家水平的结论。     

个性化决策规则获取算法及规则表示

提出了一种基于粗糙集理论的面向个性化知识的决策规则获取算法。从理论上证明了算法的正确性,给出了面向个性化的知识获取算法的描述。算法的重点在于规则合成的方法和可信度、覆盖度和规则强度计算的方法。最后通过例子说明了算法的有效性和实用性。     

基于粗糙信息向量的一种决策规则获取算法

针对不一致信息系统中决策规则获取问题,提出了一种基于粗糙信息向量方法的决策规则挖掘算法。基于粗糙信息向量,利用条件向量对决策向量的决策支持能力,直接从决策表中挖掘出符合阈值要求的尽可能简洁的决策规则,且不损失条件属性值的决策支持能力。利用该算法可以挖掘出决策系统中条件属性在各个简化层次情况下的确定性规则和缺省规则集合。理论分析和实例表明该算法在不一致信息系统中的决策规则获取上是可行的。     

关联规则挖掘在煤矿安全监测中的应用

为了从大量的煤矿安全监测数据中获取有用的知识,来指导煤矿安全预警工作,本文将关联规则挖掘算法应用于安全监测数据的数据挖掘。根据数据的特点,对数据进行了预处理后,采用了多维关联规则挖掘算法。文章设计并实现了安全监测数据的关联规则挖掘系统。通过该系统,用户在设置最小支持度和最小置信度阈值后,就可以挖掘出关联规则。     

关联规则发现在ITS中的分析与实现

针对当前智能交通系统的现状与需求,结合关联规则的研究,将关联规则发现应用到智能交通领域中,采用一种改进的Apriori算法,获取智能交通系统中实时数据的内在关联规则,为智能交通信息检索系统提供具有关联关系的数据源.以此数据源为基础,智能交通内部信息检索系统能够提供关联查询服务.     

一种基于Web用户不完备信息的规则获取方法研究

Web日志是一个很不完全且存在多样性特点的数据集,在获取决策规则的过程中经常会出现不一致、不完全规则的情况.提到了粗糙集理论,利用粗糙集理论在处理不完全知识上的特有优势来解决此种问题.首先把重要的用户行为特征值离散化作为属性值和值的约简,然后通过粗糙集缺省规则获取算法获得决策规则.其中条件属性的提取主要是一个对用户行为观察和分析的结果,而离散化处理方法就是应用粗糙集理论中的典型方法.这种处理方法有利于最后规则提取的进行,经过实例分析效果良好.     

Constrained dynamic rule induction learning

One of the known classification approaches in data mining is rule induction (RI). RI algorithms such as PRISM usually produce If-Then classifiers, which have a comparable predictive performance to other traditional classification approaches such as decision trees and associative classification. Hence, these classifiers are favourable for carrying out decisions by users and therefore they can be utilised as decision making tools. Nevertheless, RI methods, including PRISM and its successors, suffer from a number of drawbacks primarily the large number of rules derived. This can be a burden especially when the input data is largely dimensional. Therefore, pruning unnecessary rules becomes essential for the success of this type of classifiers. This article proposes a new RI algorithm that reduces the search space for candidate rules by early pruning any irrelevant items during the process of building the classifier. Whenever a rule is generated, our algorithm updates the candidate items frequency to reflect the discarded data examples associated with the rules derived. This makes items frequency dynamic rather static and ensures that irrelevant rules are deleted in preliminary stages when they don't hold enough data representation. The major benefit will be a concise set of decision making rules that are easy to understand and controlled by the decision maker. The proposed algorithm has been implemented in WEKA (Waikato Environment for Knowledge Analysis) environment and hence it can now be utilised by different types of users such as managers, researchers, students and others. Experimental results using real data from the security domain as well as sixteen classification datasets from University of California Irvine (UCI) repository reveal that the proposed algorithm is competitive in regards to classification accuracy when compared to known RI algorithms. Moreover, the classifiers produced by our algorithm are smaller in size which increase their possible use in practical applications.     

基于决策树规则的分类算法研究

在商业利益的驱动下,人们不断地深入研究决策树算法.为了提高分类的精度,提出了一种基于决策树规则的分类算法.通过C4.5决策树算法得出决策规则,计算决策规则的长度,准确率与覆盖率,对所得的决策规则依次按照规则长度与准确率的乘积大小、长度的大小、覆盖率的大小对规则集进行排序构造分类器,选择优选权最高的规则进行匹配分类.实验结果表明,与C4.5算法相比,该方法的分类精度有所提高.     

Action rule discovery from incomplete data

Action rule is an implication rule that shows the expected change in a decision value of an object as a result of changes made to some of its conditional values. An example of an action rule is ‘credit card holders of young age are expected to keep their cards for an extended period of time if they receive a movie ticket once a year’. In this case, the decision value is the account status, and the condition value is whether the movie ticket is sent to the customer. The type of action that can be taken by the company is to send out movie tickets to young customers. The conventional action rule discovery algorithms build action rules from existing classification rules. This paper discusses an agglomerative strategy that generates the shortest action rules directly from a decision system. In particular, the algorithm can be used to discover rules from an incomplete decision system where attribute values are partially incomplete. As one of the testing domains for our research we take HEPAR system that was built through a collaboration between the Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences and physicians at the Medical Center of Postgraduate Education in Warsaw, Poland. HEPAR was designed for gathering and processing clinical data on patients with liver disorders. Action rules will be used to construct the decision-support module for HEPAR.     

Bootstrapping rule induction to achieve rule stability and reduction

Most rule learning systems posit hard decision boundaries for continuous attributes and point estimates of rule accuracy, with no measures of variance, which may seem arbitrary to a domain expert. These hard boundaries/points change with small perturbations to the training data due to algorithm instability. Moreover, rule induction typically produces a large number of rules that must be filtered and interpreted by an analyst. This paper describes a method of combining rules over multiple bootstrap replications of rule induction so as to reduce the total number of rules presented to an analyst, to measure and increase the stability of the rule induction process, and to provide a measure of variance to continuous attribute decision boundaries and accuracy point estimates. A measure of similarity between rules is also introduced as a basis of multidimensional scaling to visualize rule similarity. The method was applied to perioperative data and to the UCI (University of California, Irvine) thyroid dataset. Minor Revision submitted to the Journal of Intelligent Information Systems, April 2005.     

Inductive rule generation and interpretation

Abstract

The problem of knowledge acquisition has been recognized as the major bottleneck in the development of knowledge-based systems. An encouraging approach to alleviate this problem is inductive learning. Inductive learning systems accept, as input, a set of data that represent instances of the problem domain and produce, as output, the rules of the knowledge base. Each data item is described by a set of attribute values and is assigned to a unique decision class. A common characteristic of the existing inductive learning systems, is that they are empirical in nature and do not take into account the implications of the inductive rule generation process on the performance of the resulting set of rules. That performance is assessed when the rules are used to classify new unlabelled data. This paper demonstrates that the performance of a rule set is a function of the rule generation and rule interpretation processes. These two processes are interrelated and should not be considered separately. The interrelation of rule generation and rule interpretation is analysed and suggestions to improve the performance of existing inductive learning systems, are forwarded.     

A multi-objective genetic algorithm approach to rule mining for affective product design

A novel multi-objective genetic algorithm (GA)-based rule-mining method for affective product design is proposed to discover a set of rules relating design attributes with customer evaluation based on survey data. The proposed method can generate approximate rules to consider the ambiguity of customer assessments. The generated rules can be used to determine the lower and upper limits of the affective effect of design patterns. For a rule-mining problem, the proposed multi-objective GA approach could simultaneously consider the accuracy, comprehensibility, and definability of approximate rules. In addition, the proposed approach can deal with categorical attributes and quantitative attributes, and determine the interval of quantitative attributes. Categorical and quantitative attributes in affective product design should be considered because they are commonly used to define the design profile of a product. In this paper, a two-stage rule-mining approach is proposed to generate rules with a simple chromosome design in the first stage of rule mining. In the second stage of rule mining, entire rule sets are refined to determine solutions considering rule interaction. A case study on mobile phones is used to demonstrate and validate the performance of the proposed rule-mining method. The method can discover rule sets with good support and coverage rates from the survey data.     

An improved random forest-based rule extraction method for breast cancer diagnosis

Breast cancer has been becoming the main cause of death in women all around the world. An accurate and interpretable method is necessary for diagnosing patients with breast cancer for well-performed treatment. Nowadays, a great many of ensemble methods have been widely applied to breast cancer diagnosis, capable of achieving high accuracy, such as Random Forest. However, they are black-box methods which are unable to explain the reasons behind the diagnosis. To surmount this limitation, a rule extraction method named improved Random Forest (RF)-based rule extraction (IRFRE) method is developed to derive accurate and interpretable classification rules from a decision tree ensemble for breast cancer diagnosis. Firstly, numbers of decision tree models are constructed using Random Forest to generate abundant decision rules available. And then a rule extraction approach is devised to detach decision rules from the trained trees. Finally, an improved multi-objective evolutionary algorithm (MOEA) is employed to seek for an optimal rule predictor where the constituent rule set is the best trade-off between accuracy and interpretability. The developed method is evaluated on three breast cancer data sets, i.e., the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, Wisconsin Original Breast Cancer (WOBC) dataset, and Surveillance, Epidemiology and End Results (SEER) breast cancer dataset. The experimental results demonstrate that the developed method can primely explain the black-box methods and outperform several popular single algorithms, ensemble learning methods, and rule extraction methods from the view of accuracy and interpretability. What is more, the proposed method can be popularized to other cancer diagnoses in practice, which provides an option to a more interpretable, more accurate cancer diagnosis process.     

一种基于粗糙集理论的智能故障诊断新方法

论文针对基于规则的故障源分离与定位方法中的一个关键问题,即诊断规则的获取,利用粗糙集的基本原理构造出了一种用于规则提取的新方法,其中包括了用于对故障决策表,即故障字典,进行属性约简的改进算法和属性值的顺序约简算法。该方法能够迅速从故障字典中提取出诊断规则,并揭示出故障信息内在的冗余性。最后实例应用的结果表明了该方法的有效性,尤其是在不完全信息情况下的有效性。     

基于属性分辨度的最大相容块规则提取算法

提出一种基于属性分辨度的不完备决策表规则提取算法, 它是一种例化方向的方法. 首先从空集开始, 逐步 选择当前最重要的条件属性对对象集分类, 从广义决策值唯一的相容块提取确定规则, 从其他的相容块提取不确定 规则; 然后设计属性必要性判断步骤去除每条规则的冗余属性; 最后通过规则约简过程来简化所获得的规则, 增强规 则的泛化能力. 实验结果表明, 所提出的算法效率更高, 并且所获得的规则简洁有效.