自动提取词汇化树邻接文法

提出了一种从宾州中文语料库中自动提取词汇化树邻接文法(LTAG)的算法。该算法的主要思想是从词汇化树库中归纳出三种类型的词汇化树,然后利用了中心词驱动短语结构文法的方法从语料库自动提取结构合理的词汇化树;最后由语言规则对不合法的词汇化树进行过滤。与手工创建词汇化树邻接文法相比,它需要较少的人力,并且避免了人工创建词汇化树可能造成的遗漏或不一致现象。     

基于图文法制导的ER 图自动绘制技术

逻辑模型可视化对于软件开发和技术研究具有重要的意义．提出了一种基于图文法制导ER图自动绘制技术,并对图文法进行扩展命名为ER图文法,它克服了在原有建模工具中绘制ER图图幅数量有限和不易更改的缺点,并从语义上对实体关系及ER图绘制规则进行描述,提出ER图自动布局和布线算法,实现ER图的自动绘制．     

网络入侵检测的GEP规则提取算法研究

针对基于机器学习网络入侵检测存在的未知攻击检测率低、规则多而复杂导致检测效率不高等问题,提出了基于约束的基因表达式编程(GEP)规则提取算法(CGREA).用GEP模式表示入侵检测规则,定义了约束文法对规则个体进行约束,以满足规则的充分性和封闭性.CGREA算法限定GEP规则基因头部各类符号的随机选择数目比例.并采用精英策略以保证算法收敛性.用KDDCUP'99数据集对CGREA算法提取的入侵检测规则进行评估,总攻击检测率为91.36%,其中有3种未知攻击的检测率超过88%.结果表明,CGREA算法能在较小种群和有限代数内提取出简单而有效的规则,未知攻击检测率和检测性能也得到提高.     

一种层次化的LSD规则体系及其分析算法

本文提出了一种基于词汇属性结构描述和规则继承的层次化LSD规则体系,讨论了该规则体系下的规则搜索策略和词汇化规则索引的实现方法,并在此基础上首次给出了LSD文法的非确定性分析算法。该规则系统具有从传统属性文法到现代词汇文法的可伸缩性,同时较好地解决了线性规则库中复杂的规则交互问题。     

地震图中地震波形的自动提取方法研究

讨论了地震图中地震波形的自动提取方法,提出了一种适用性较强的水平校正和水平消除算法,以及一种利用峰值与零点进行波形校正的算法,归纳了波形提取中的各种波形模式并给出了智能化的波形搜索规则。     

自动生成数据挖掘算法的研究与应用

主要研究自动生成数据挖掘算法的解决方案.采用遗传算法对数据挖掘中的关联规则进行自动挖掘,提出一种基于遗传算法的关联规则自动提取算法,并结合电视购物项目,给出了该算法的实例验证.最后,通过与传统的Apriori算法比较,验证了该算法的高效性.     

启发式知识获取方法研究

归纳学习是解决知识自动获取的有效方法，针对ID3算法、基于粗集的归纳学习以及其它一些归纳学习方法存在的问题，提出了一种新的归纳学习算法ITIL。此算法用信息增益为启发式，选择尽量少的重要属性或组合，以可分辨性为依据提取规则，许多实例表明，这些规则不仅简单，而且冗余小，作为知识获取模块的一部分，ITIL已被集成到一个“基于知识发现的医疗诊断辅助系统”动态知识库子系统中。     

基于本体论的Web信息抽取

以本体论为基础，以所要提取的信息的层次结构作为信息提取的路径，定义了Web页面的信息项本体，并自动解析生成Web页面的结构本体．通过对这两个本体进行对比，构造了一种归纳学习算法来半自动地生成信息提取规则，对Web页面的信息提取具有较高的效率．     

基于GP的神经网络学习规则的发现

提出用遗传编程(GP)方法发现新的神经网络学习规则,并通过实验得到了几个新的学习规则;另外讨论了通过文法产生式的设计来改进对该问题的研究.     

基于GP算法的知识发现系统

给出一种基于GP（Genetic Programming,遗传编程）算法的知识发现系统,系统自动根据查询集访问数据库,从而发掘出数据库中隐含的知识。对知识发掘过程进行详细描述,并提出了一种用遗传编程（GP）来进行数据挖掘的方法,GP个体由数据库查询组成,并给出了一个实验例子。     

Association rule mining using genetic programming to provide feedback to instructors from multiple‐choice quiz data

This paper proposes the application of association rule mining to improve quizzes and courses. First, the paper shows how to preprocess quiz data and how to create several data matrices for use in the process of knowledge discovery. Next, the proposed algorithm that uses grammar‐guided genetic programming is described and compared with both classical and recent soft‐computing association rule mining algorithms. Then, different objective and subjective rule evaluation measures are used to select the most interesting and useful rules. Experiments have been carried out by using real data of university students enrolled on an artificial intelligence practice Moodle's course on the CLIPS programming language. Some examples of these rules are shown, together with the feedback that they provide to instructors making decisions about how to improve quizzes and courses. Finally, starting with the information provided by the rules, the CLIPS quiz and course have been updated. These innovations have been evaluated by comparing the performance achieved by students before and after applying the changes using one control group and two different experimental groups.     

基于产生式的多语言程序理解的算法

提出一种程序理解实现方案,通过将程序设计语言的词法规则和语法规则以产生式表示,设计基于产生式多语言程序处理算法,将产生式自动转化为对应的词法和语法规则函数,使得不同的程序设计语言规则可以采用统一的方式描述,从而可用一套程序理解系统实现对多种程序设计语言的程序理解,以后增加某种程序设计语言的程序理解,只要定义对应程序设计语言的词法和语法规则产生式即可实现,有效地解决了程序理解系统的共享性难题。     

Knowledge discovery with classification rules in a cardiovascular dataset

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.     

基于递阶遗传算法模糊加权神经网络的模糊规则自动获取

针对模糊规则的自动获取一直是模糊系统的一个瓶颈问题,提出一种基于递阶结构的混合编码遗传算法与进化规划相结合的模糊加权神经网络学习新算法,利用该算法同时优化模糊加权神经网络的结构和参数,最后说明了从网络中提取模糊规则的方法,从而自动获得最优的模糊规则。分析和实验结果表明,本文方法在规则提取和分类准确性等方面比其他方法更好。     

基于形式描述方法的文法进化算法研究

本文应用基于形式文法的方法,结合计算机科学中新兴有效的进化计算优化方法,进行了文法进化算法的有效性探索。通过运用巴科斯描述文法的四元组,强调了BNF产生式的设计,充分利用遗传操作来优化求解问题。并以有机化合物的分解(以离解能适应值进行适应度评价)为例子进行了算法设计,通过适应度函数择优,复制、交换和突变操作获得了实验结果。经过对结果的分析比较证明了算法的有效性。可以说是文法进化算法的成功探索。     

A new approach to the extraction of ANN rules and to their generalization capacity through GP

Various techniques for the extraction of ANN rules have been used, but most of them have focused on certain types of networks and their training. There are very few methods that deal with ANN rule extraction as systems that are independent of their architecture, training, and internal distribution of weights, connections, and activation functions. This article proposes a methodology for the extraction of ANN rules, regardless of their architecture, and based on genetic programming. The strategy is based on the previous algorithm and aims at achieving the generalization capacity that is characteristic of ANNs by means of symbolic rules that are understandable to human beings.     

基于TS模糊神经网络的Fuzzy规则自动获取研究

Fuzzy规则的获取一直是模糊智能系统的一个瓶颈。醉在深入研究TS模糊神经网络的物理意义的基础上,给出了使用遗传算法优化模糊规则集的算法并提出了从训练后的TS模糊神经网络中抽取Fuzzy规则的可操作方法。分析和实验证明,这种方法可以实现且是有效的,对于Fuzzy规则自动获取的研究具有积极的借鉴意义。     

DRAL: a tool for discovering relevant e-activities for learners

Web-based educational systems routinely collect vast quantities of data on students’ e-activity generating log files that offer researchers unique opportunities to apply data mining techniques and discover interesting information to improve the learning process. This paper proposes a friendly and intuitive tool called DRAL to detect the most relevant e-activities that a student needs to pass a course based on features extracted from logged data in an education web-based system. The method uses a more flexible representation of the available information based on multiple instance learning to prevent the appearance of a great number of missing values and is based on a multi-objective grammar guided genetic programming algorithm which obtains simple and clear classification rules which are markedly useful to identify the number, type and time of e-activities more relevant so that a student has a high probability to pass a course. To validate this approach, our proposal is compared with the most traditional proposals in multiple instance learning over the years. Experimental results demonstrate that the approach proposed successfully improves the accuracy of previous models by finding a balance between specificity and sensitivity values.     

汉语概率型上下文无关语法的自动推导

本文提出了一种汉语概率型上下文无关语法的自动推导方法，它在匹配分析机制上实现了无指导的ＥＭ迭代训练算法，并通过对训练语料的自动短语界定预处理以及在集成不同知识源基础上构造合适始规则集