一种基于统计排序的网络流量特征选择方法

利用网络流量的统计特征进行流量的分类识别需要从众多的特征中选取最优特征集合,以避免冗余和不相关特征造成的系统模型复杂度过高、分类准确率和效率下降等问题。针对该问题,提出一种基于统计排序的网络流量特征选择方法。首先利用基于统计方法定义的特征选择系数生成初始特征子集,再将基于分类准确率构建的特征影响系数作为特征评估排序的依据,对初始特征子集进行二次特征选择,生成最优特征子集。实验结果表明,该方法在保证分类整体准确率的同时有效减少了流量统计特征的个数,在分类效果、效率以及稳定性之间实现了较好的平衡。     

一种新的快速特征选择和数据分类方法

针对数据分类问题提出一种新型高效的特征选择和规则提取方法.首先通过减少初始区间数量改进Chi-Merge离散化方法,再采用改进的Chi-Merge离散化连续型特征变量;特征离散化后,统计样本数据在每个特征子集划分下的频数表,并根据频数表计算数据不一致率,再利用顺序前向最优搜索的方法,快速确定特征数量由小到大的每一个最优特征子集;根据特征子集对应的数据不一致率差异最小化原则,完成特征个数最小化的最优特征子集筛选;根据最优特征子集的数据频数表,可直接提取数据分类规则.实验表明,快速提取的规则可获得较好的分类效果.基于该特征选择方法,提出一种面向分布式同构数据的快速分类模型,不但具有良好的分类效果,还支持对样本数据内容的隐私保护.     

基于粗糙集约简的神经网络集成及其遥感图像分类应用

为降低集成特征选择方法的计算复杂性,提出了一种基于粗糙集约简的神经网络集成分类方法。该方法首先通过结合遗传算法求约简和重采样技术的动态约简技术,获得稳定的、泛化能力较强的属性约简集;然后,基于不同约简设计BP网络作为待集成的基分类器,并依据选择性集成思想,通过一定的搜索策略,找到具有最佳泛化性能的集成网络;最后通过多数投票法实现神经网络集成分类。该方法在某地区Landsat 7波段遥感图像的分类实验中得到了验证,由于通过粗糙集约简,过滤掉了大量分类性能欠佳的特征子集,和传统的集成特征选择方法相比,该方法时     

基于粗糙集约简的神经网络集成及其遥感图像分类应用

为降低集成特征选择方法的计算复杂性,提出了一种基于粗糙集约简的神经网络集成分类方法。该方法首先通过结合遗传算法求约简和重采样技术的动态约简技术,获得稳定的、泛化能力较强的属性约简集;然后,基于不同约简设计BP网络作为待集成的基分类器,并依据选择性集成思想,通过一定的搜索策略,找到具有最佳泛化性能的集成网络;最后通过多数投票法实现神经网络集成分类。该方法在某地区Landsat 7波段遥感图像的分类实验中得到了验证,由于通过粗糙集约简,过滤掉了大量分类性能欠佳的特征子集,和传统的集成特征选择方法相比,该方法时间开销少,计算复杂性低,具有满意的分类性能。     

改进混合二进制蝗虫优化特征选择算法

特征选择是从数据集的原始特征中选出最优或较优特征子集,从而在加快分类速度的同时提高分类准确率.提出了一种改进的混合二进制蝗虫优化特征选择算法:通过引入步长引导个体位置变化的二进制转化策略,降低了进制转换的盲目性,提高了算法在解空间中的搜索性能;通过引入混合复杂进化方法,将蝗虫群体划分子群并独立进化,提高了算法的多样性,降低了早熟收敛的概率.采用改进算法对UCI部分数据集进行特征选择,使用K-NN分类器对特征子集进行分类评价,实验结果表明:与基本二进制蝗虫优化算法、二进制粒子群优化算法和二进制灰狼优化算法相比,改进算法具有较优的搜索性能、收敛性能与较强的鲁棒性,能够获得更好的特征子集,取得更好的分类效果.     

结合粗糙集和禁忌搜索的网络流量特征选择

针对网络流量特征属性的优化选择问题,提出了一种结合粗糙集和禁忌搜索的网络流量特征选择方法(RS-TS).该方法通过粗糙集算法对网络流量特征属性进行约简,将所得到的特征子集作为禁忌搜索的初始解,并利用禁忌搜索得到最优特征子集.实验验证RS-TS方法优于基于GA的特征选择方法和基于IG的特征选择方法,能够有效地去除网络流量的冗余特征属性,提高网络流量分类精度.     

面向特征选择问题的协同演化方法

特征选择技术是机器学习和数据挖掘任务的关键预处理技术。传统贪婪式特征选择方法仅考虑本轮最佳特征,从而导致获取的特征子集仅为局部最优,无法获得最优或者近似最优的特征集合。进化搜索方式则有效地对特征空间进行搜索,然而不同的进化算法在搜索过程中存在自身的局限。本文吸取遗传算法（GA）和粒子群优化算法（PSO）的进化优势,以信息熵度量为评价,通过协同演化的方式获取最终特征子集。并提出适用于特征选择问题特有的比特率交叉算子和信息交换策略。实验结果显示,遗传算法和粒子群协同进化（GA-PSO）在进化搜索特征子集的能力和具体分类学习任务上都优于单独的演化搜索方式。进化搜索提供的组合判断能力优于贪婪式特征选择方法。     

混合式的K-匿名特征选择算法

K-匿名算法通过对数据的泛化、隐藏等手段使得数据达到K-匿名条件,在隐藏特征的同时考虑数据的隐私性与分类性能,可以视为一种特殊的特征选择方法,即K-匿名特征选择。K-匿名特征选择方法结合K-匿名与特征选择的特点使用多个评价准则选出K-匿名特征子集。过滤式K-匿名特征选择方法难以搜索到所有满足K-匿名条件的候选特征子集,不能保证得到的特征子集的分类性能最优,而封装式特征选择方法计算成本很大,因此,结合过滤式特征排序与封装式特征选择的特点,改进已有方法中的前向搜索策略,设计了一种混合式K-匿名特征选择算法,使用分类性能作为评价准则选出分类性能最好的K-匿名特征子集。在多个公开数据集上进行实验,结果表明,所提算法在分类性能上可以超过现有算法并且信息损失更小。     

基于筛选-优化的基因选择

先按照相关性系数标准对样本所包含的全体基因进行筛选,降低冗余的同时有利于缩小优化算法的搜索范围;然后在筛选结果上采用蚁群优化策略进行分类相关基因子集的选择,并利用样本聚类效果作为优化目标函数,在保证分类准确度的同时大大降低了基因选择方法的计算复杂度。实验结果表明,提出的基因选择方法能在相当短的时间内选出与分类相关的基因子集。     

基于BPSO-SVM的网络入侵特征选择和检测

采用改进的二进制粒子群优化进行入侵特征子集选择,粒予群中每个粒子代表。个选择的特征子集,结合支持向世机使用该特征子集所对应的数据集进行分类,正确分类结果作为该粒子的适应度,通过粒子群优化实现最优入侵特征选择。改进的BPSO方沾中通过引入粒于群依概率整体变异来避免陷入局部最优,同时采用粒子禁忌搜索列表来扩大粒子搜索范围和避免重复计算;SVM中采用基于粒度的网格搜索来获得最优核参数。最后用KDD99标准数据集进行实验研究,结果表明该方法能获得满意的检测效果。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.