考虑不平衡指数的不平衡数据集分类设计方法

数据集类别不平衡问题是分类领域的重要问题之一,每个数据集的不平衡指数都与其自身有着紧密的联系,是数据集的重要标志。面对不平衡数据集分类设计问题,提出了一种改进AdaBoost算法（enhanced AdaBoost,E-AdaBoost）。该算法将不平衡指数和不平衡数据集中较为重要的少数类分类正确率考虑到算法的迭代过程中,改进了基分类器的权重更新策略,进而提高对不平衡数据集的分类性能。基于E-AdaBoost的不平衡数据集分类设计方法可以根据样本的不平衡指数,确定基分类器的权重参数,进而提高分类器性能。利用该方法,结合多个经典分类器,在人工数据集和标准数据集上进行实验分析,并对比相关方法,结果表明,基于E-AdaBoost的不平衡数据集分类设计方法能够有效提高不平衡数据集的分类性能。     

一种面向不平衡数据分类的组合剪枝方法

传统的数据分类算法多是基于平衡的数据集创建,对不平衡数据分类时性能下降,而实践表明组合选择能有效提高算法在不平衡数据集上的分类性能。为此,从组合选择的角度考虑不平衡类学习问题,提出一种新的组合剪枝方法,用于提升组合分类器在不平衡数据上的分类性能。使用Bagging建立分类器库,直接用正类(少数类)实例作为剪枝集,并通过MBM指标和剪枝集,从分类器库中选择一个最优或次优子组合分类器作为目标分类器,用于预测待分类实例。在12个UCI数据集上的实验结果表明,与EasyEnsemble、Bagging和C4.5算法相比,该方法不但能大幅提升组合分类器在正类上的召回率,而且还能提升总体准确率。     

基于Boosting的不平衡数据分类算法研究

研究基于boosting的不平衡数据分类算法,归纳分析现有算法,在此基础上提出权重采样boosting算法。对样本进行权重采样,改变原有数据分布,从而得到适用于不平衡数据的分类器。算法本质是利用采样函数调整原始boosting损失函数形式,进一步强调正样本的分类损失,使得分类器侧重对正样本的有效判别,提高正样本的整体识别率。算法实现简单,实用性强,在UCI数据集上的实验结果表明,对于不平衡数据分类问题,权重采样boosting优于原始boosting及前人算法。     

一种面向不平衡数据的结构化SVM集成分类器

为改进面向不平衡数据的SVM分类器性能,以结构化SVM为基础,提出一种基于代价敏感的结构化支持向量机集成分类器模型.该模型首先通过训练样本的聚类,得到隐含在数据中的结构信息,并对样本进行初始加权.运用AdaBoost策略对各样本的权重进行动态调整,适当增大少数类样本的权重,使小类中误分的样本代价增大,以此来改进不平衡数据的分类性能.实验结果表明,该算法可有效提高不平衡数据的分类性能.     

集成学习算法在不平衡分类中的应用研究

提出一种基于训练集分解的不平衡分类算法,该算法使用能输出后验概率的支持向量机作为分类器,使用基于测度层次信息源合并规则实现分类器的集成.在4个不同领域的不平衡数据集上的仿真实验表明:该算法有效提高分类器对正类样本的正确率,同时尽量减少对负类样本的误判.实验结果验证集成学习算法处理不平衡分类问题的有效性.     

Bagging组合的不平衡数据分类方法

提出一种基于Bagging组合的不平衡数据分类方法CombineBagging,采用少数类过抽样算法SMOTE进行数据预处理,在此基础上利用C-SVM、径向基函数神经网络、Random Forests 3种不同的基分类器学习算法,分别对采样后的数据样本进行Bagging集成学习,通过投票规则集成学习结果。实验结果表明,该方法能够提高少数类的分类准确率,有效处理不平衡数据分类问题。     

一种代价敏感的旋转森林分类算法

分类器集成通过将弱学习器提升为强学习器,提高了分类器分类的准确性.但当它面对不平衡数据问题时,虽然比单个分类器效果要好,但依旧无法达到预期效果.基于此提出了一种代价敏感的旋转森林算法(CROF),利用旋转森林进行数据预处理,并将代价函数引入基分类器构造中,最终获得面向不平衡数据问题的新的集成分类器.经实验表明,CROF算法能够有效提高少数类的分类能力,可以较好处理不平衡问题.     

基于混合采样与Random_Stacking的软件缺陷预测

现有的软件缺陷预测方法面临数据类别不平衡性、高维数据处理等问题。如何有效解决上述问题已成为目前相关领域的研究热点。针对软件缺陷预测所面临的类别不平衡、预测精度低等问题,本文提出一种基于混合采样与Random_Stacking的软件缺陷预测算法DP_HSRS。DP_HSRS算法首先采用混合采样算法对不平衡数据进行平衡化处理;然后在该平衡数据集上采用Random_Stacking算法进行软件缺陷预测。Random_Stacking算法是对传统Stacking算法的一种有效改进,它通过融合多个经典的分类算法以及Bagging机制构建多个Stacking分类器,对多个Stacking分类器进行投票,得到一个集成分类器,最后利用该集成分类器对软件缺陷进行预测。通过在NASA MDP数据集上的实验结果表明,DP_HSRS算法的性能优于现有的算法,具有更好的缺陷预测性能。     

基于多类指数损失函数逐步添加模型的改进多分类AdaBoost算法

多类指数损失函数逐步添加模型（SAMME）是一种多分类的AdaBoost算法,为进一步提升SAMME算法的性能,针对使用加权概率和伪损失对算法的影响进行研究,在此基础上提出了一种基于基分类器对样本有效邻域分类的动态加权AdaBoost算法SAMME.RD。首先,确定是否使用加权概率和伪损失;然后,求出待测样本在训练集中的有效邻域;最后,根据基分类器针对有效邻域的分类结果确定基分类器的加权系数。使用UCI数据集进行验证,实验结果表明：使用真实的错误率计算基分类器加权系数效果更好;在数据类别较少且分布平衡时,使用真实概率进行基分类器筛选效果较好;在数据类别较多且分布不平衡时,使用加权概率进行基分类器筛选效果较好。所提的SAMME.RD算法可以有效提高多分类AdaBoost算法的分类正确率。     

一种面向不平衡数据集的组合分类算法

为了提高不平衡数据集中少数类的分类准确率,文章对组合分类算法进行了研究,提出了一种新的组合分类算法WDB.该算法采用决策树C4.5和朴素贝叶斯两种不同的分类器作为基分类器,选择精确度(precision)作为权值,根据不同的训练集,通过"权值学习"的方式自动调整各基分类器的权值大小,然后,结合各基分类器的预测结果,利用加权平均法进行代数组合,构造出一种新的分类算法WDB.最后,以开放的不平衡数据集作为数据源,利用常见的性能评价指标进行实验验证.实验结果证明,在组合分类算法中引入"权值学习"能够发挥基分类器对于特定数据类型的分类优势,提高预测结果的准确率.WDB算法对不平衡数据集分类的性能优于决策树C4.5算法、朴素贝叶斯算法及随机森林算法,能够有效提升不平衡数据集中少数类的分类准确率.     

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.