On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality

The classification problem is considered in which an outputvariable y assumes discrete values with respectiveprobabilities that depend upon the simultaneous values of a set of input variablesx = {x_1,....,x_n}. At issue is how error in the estimates of theseprobabilities affects classification error when the estimates are used ina classification rule. These effects are seen to be somewhat counterintuitive in both their strength and nature. In particular the bias andvariance components of the estimation error combine to influenceclassification in a very different way than with squared error on theprobabilities themselves. Certain types of (very high) bias can becanceled by low variance to produce accurate classification. This candramatically mitigate the effect of the bias associated with some simpleestimators like naive Bayes, and the bias induced by thecurse-of-dimensionality on nearest-neighbor procedures. This helps explainwhy such simple methods are often competitive with and sometimes superiorto more sophisticated ones for classification, and whybagging/aggregating classifiers can often improveaccuracy. These results also suggest simple modifications to theseprocedures that can (sometimes dramatically) further improve theirclassification performance.     

基于神经网络集成技术的运动想像脑电识别方法

针对运动想像脑电信号,提出一种基于神经网络集成技术的识别方法.该方法通过离散小波变换(DWT)抽取想像左、右手运动的主要特征,采用Bagging算法构建神经网络集成(NNE)模型,并选取相对多数投票法获得网络集成的输出结果.在"BCI Competition 2003"竞赛数据集上的实验结果表明,该方法得到了比基于单个神经网络的脑电信号识别方法更高的识别率,同时,降低了个体神经网络的配置难度,提高了系统的泛化能力.     

Global and Local (Glocal) Bagging Approach for Classifying Noisy Dataset

Learning from noisy data is a challenging task for data mining research. In this paper, we argue that for noisy data both global bagging strategy and local bagging strategy su er from their own inherent disadvantages and thus cannot form accurate prediction models. Consequently, we present a Global and Local Bagging (called Glocal Bagging:GB) approach to tackle this problem. GB assigns weight values to the base classi ers under the consideration that: (1) for each test instance I_x, GB prefers bags close to I_x, which is the nature of the local learning strategy; (2) for base classi ers, GB assigns larger weight values to the ones with higher accuracy on the out-of-bag, which is the nature of the global learning strategy. Combining (1) and (2), GB assign large weight values to the classi ers which are close to the current test instance I_x and have high out-of-bag accuracy. The diversity/accuracy analysis on synthetic datasets shows that GB improves the classi er ensemble's performance by increasing its base classi er's accuracy. Moreover, the bias/variance analysis also shows that GB's accuracy improvement mainly comes from the reduction of the bias error. Experiment results on 25 UCI benchmark datasets show that when the datasets are noisy, GB is superior to other former proposed bagging methods such as the classical bagging, bragging, nice bagging, trimmed bagging and lazy bagging.     

环状物新检测方法及在敏感图像识别中的应用

为了克服传统的Hough变换类环状物体检测的局限,提出了1种结合物体形状与外观特征的环状物体识别检测算法.识别算法使用cascade结构,分别使用灰度、纹理以及外观综合特征,按Bagging的方法训练产生一组弱分类器.这些弱分类器串接而成,并结合局部物体分割,逐个处理当前扫描窗口.相比于传统的Hough算法,新方法具有更快的检测速度.选择敏感图像作为实验对象,采集数据进行训练和检测,实验结果表明,新方法具有明显的性能优势.使用更全面的物体外观信息,按Bagging产生弱分类器的组合,能够在提高环状物体的检测性能的同时,获得理想的处理速度.     

Evolutionary under-sampling based bagging ensemble method for imbalanced data classification

In the class imbalanced learning scenario, traditional machine learning algorithms focusing on optimizing the overall accuracy tend to achieve poor classification performance especially for the minority class in which we are most interested. To solve this problem, many effective approaches have been proposed. Among them, the bagging ensemble methods with integration of the under-sampling techniques have demonstrated better performance than some other ones including the bagging ensemble methods integrated with the over-sampling techniques, the cost-sensitive methods, etc. Although these under-sampling techniques promote the diversity among the generated base classifiers with the help of random partition or sampling for the majority class, they do not take any measure to ensure the individual classification performance, consequently affecting the achievability of better ensemble performance. On the other hand, evolutionary under-sampling EUS as a novel undersampling technique has been successfully applied in searching for the best majority class subset for training a good-performance nearest neighbor classifier. Inspired by EUS, in this paper, we try to introduce it into the under-sampling bagging framework and propose an EUS based bagging ensemble method EUS-Bag by designing a new fitness function considering three factors to make EUS better suited to the framework. With our fitness function, EUS-Bag could generate a set of accurate and diverse base classifiers. To verify the effectiveness of EUS-Bag, we conduct a series of comparison experiments on 22 two-class imbalanced classification problems. Experimental results measured using recall, geometric mean and AUC all demonstrate its superior performance.     

基于改进聚类和加权bagging的多模型软测量建模

针对化工生产过程中软测量模型估计精度的问题,提出一种基于改进聚类和加权bagging的多模型建模方法。该方法在传统FCM聚类的基础上,利用K-近邻处理进一步降低错分率,改善聚类效果;通过相关性分析对训练样本集进行特征分组,将原始集划分为多个特征集;最后根据加权bagging的集成学习算法,融合支持向量机自适应地实现多模型建模。仿真结果表明,该建模方法可以合理地加权分配特征子模型,使得模型估计精度得到提高,具有更强的泛化能力。     

基于电液一体化的包装机自动装袋装置设计

运用电液一体化知识,设计集装料、封口及运输等于一体的包装机,重点研究了自动装袋装置的组成、工作原理以及液压动作原理等内容。此装置具有自动供袋、记数装袋、折口缝边等功能,实践证明:该装置整机性能稳定可靠,自动化程度高,处理速度快,适合于大批量生产,包装质量好,能够连续工作。     

Analytical studies on woven fabrics’ bagging performance affected by the material,yarn, and fabric parameters

The present work focuses on investigating the bagging behavior of woven fabrics produced from viscose and polyester/viscose yarns. In this paper, the bagging deformation – in terms of different bagging parameters (bagging resistance, bagging fatigue, bagging hysteresis, and residual bagging hysteresis) – has been interpreted by fabric’s physical and mechanical properties which were obtained from FAST system. The statistical analysis revealed that there is a significant correlation between the bagging parameters. Also, it was concluded that the samples’ physical and mechanical properties correlate significantly with bagging parameters. Additionally, the effects of four different variables such as material type, spinning system, weave pattern, and weft density on bagging parameters have been studied. Considering the high correlations between all the bagging parameters, it was decided to analyze only the parameter bagging fatigue in this paper. The results showed that all the fabric variables except the parameter weft density have significant effects on bagging fatigue performance.     

Sheet forming of woven fabric composite by combined cyclic stretch and deep drawing

The manufacturing of composites often requires transformation of 2-D fabrics into various structural 3-D shapes through preform processing without over straining of the yarns or wrinkling. Sheet forming of woven fabric composite by combined cyclic stretch and deep drawing was suggested. In order to prevent the wrinkling, the geometrical and mechanical properties of fabric should be chosen to get the final form of the prepregs beforehand. Geometrical investigation of the final bagged form dimensions are based on geometrical aspects of the wrapping of the fabric. In this study, an approach was proposed to form the reinforcement fabric composite through the dynamic bagging of the fabric by the application of cyclic stretching on the fabric during formation of 3-D reinforcement structure in hot environment. The dimensions of the flat fabric were calculated and the parameters of the forming process analyzed in two cases: hemisphere and flat top cone shape.     

气动书籍包装机的设计与研究

为提高书籍包装的效率,设计一种结构新颖、性能稳定的气动书籍包装机,实现书籍自动装袋包装的功能。利用MATLAB和ADAMS建立运动力学数学模型并进行仿真,利用ANAYS进行关键部件的有限元分析,最后进行实际测试。该包装机关键零部件满足强度与硬度要求,减少了书籍包装的时间,包装成功率高。该气动书籍包装机性能稳定,可以连续高效地实现书籍包装功能,具有较高的经济效益。