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Traditional Multiple Empirical Kernel Learning (MEKL) expands the expressions of the sample and brings better classification ability by using different empirical kernels to map the original data space into multiple kernel spaces. To make MEKL suit for the imbalanced problems, this paper introduces a weight matrix and a regularization term into MEKL. The weight matrix assigns high misclassification cost to the minority samples to balanced misclassification cost between minority and majority class. The regularization term named Majority Projection (MP) is used to make the classification hyperplane fit the distribution shape of majority samples and enlarge the between-class distance of minority and majority class. The contributions of this work are: (i) assigning high cost to minority samples to deal with imbalanced problems, (ii) introducing a new regularization term to concern the property of data distribution, (iii) and modifying the original PAC-Bayes bound to test the error upper bound of MEKL-MP. Through analyzing the experimental results, the proposed MEKL-MP is well suited to the imbalanced problems and has lower generalization risk in accordance with the value of PAC-Bayes bound. 相似文献
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针对工业控制系统传统单一检测算法模型对不同攻击类型检测率和检测速度不佳的问题,提出一种优化支持向量机和K-means++算法结合的入侵检测模型。首先利用主成分分析法(PCA)对原始数据集进行预处理,消除其相关性;其次在粒子群优化(PSO)算法的基础上加入自适应变异过程避免在训练的过程中陷入局部最优解;然后利用自适应变异粒子群优化(AMPSO)算法优化支持向量机的核函数和惩罚参数;最后利用密度中心法改进K-means算法与优化后的支持向量机组合成入侵检测模型,从而实现工业控制系统的异常检测。实验结果表明,所提方法在检测速度和对各类攻击的检测率上得到明显提升。 相似文献
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The need for feature selection and dimension reduction is felt as a fundamental step in security assessment of large power systems in which the number of features representing the state of power grids dramatically increases. These large amounts of attributes are not proper to be used for computational intelligence (CI) techniques as inputs, because it may lead to a time consuming procedure with insufficient results and they are not suitable for on-line purposes and updates.This paper proposes a combined method for an online voltage security assessment in which the dimension of the token data from phasor measurement units (PMUs) is reduced by principal component analysis (PCA). Then, the features with different stability indices are put into several categories and feature selection is done by correlation analysis in each category. These selected features are then given to decision trees (DTs) for classification and security assessment of power systems.The method is applied to 39-bus test system and a part of Iran power grid. It is seen from the results that the DTs with reduced data have simpler splitting rules, better performance in saving time, reasonable DT error and they are more suitable for constant updates. 相似文献
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为了提供高性能和柔性,操作系统内核应该只保留最小的功能,今天的操作系统大而低效,更缺乏柔性,我们提出了保护分散式操作系统抽象方法,集成了分散式操作系统抽象和保护抽象的方法,这种新的安全方式在分散式抽象系统里共享用户级的抽象,能够使非特权,不信任的任务在运行时定义并安全的共享生成的抽象,它在对相同的抽象重复调用时,通过消除上下文切换的需求和对通常情况进行优化,可获得较好的柔性,保护分散式抽象的设计强调简单并正确性证明,易于理解和使用,文章介绍了保护分散式操作系统抽象方法,并运用该方法开发了一个基于共享库的原型操作系统-ExLinux/LibOS. 相似文献
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为了准确、快速地进行缺陷识别,介绍了一种新型的前馈神经网络模型,即径向基概率神经网络。与以往的算法相比,该方法具有分类识别精度高且速度快的优点。仿真获得了很好的结果。 相似文献
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Javier Roca-Pardiñas Carmen Cadarso-Suárez María J. Lado 《Computational statistics & data analysis》2008,52(4):1958-1970
In many applications, the joint effect of two continuous covariates on the target binary response may vary across groups defined by levels of a given factor. A testing procedure that would enable this type of surface-by-factor interactions to be detected has been designed. To accomplish this goal, a logistic generalized additive model (GAM) with bivariate continuous interactions varying across groups defined by levels of a factor is considered. A local scoring algorithm based on local linear kernel smoothers was implemented to estimate the proposed logistic GAM. Bootstrap resampling techniques were used for the purpose of testing for factor-by-surface interactions. Given the high computational cost involved, binning techniques were used to speed up computation in the estimation and testing processes. The adequacy of the bootstrap-based test was assessed by means of a simulation study. If a factor-by-surface interaction is detected in the model, it is then established that the use of the odds-ratio curves is very useful in obtaining a direct interpretation of the fitted model. The benefits of using this methodology when analyzing real data are illustrated by applying the technique to the outputs produced by a computerized system dedicated to the early detection of breast cancer. 相似文献