新型含噪数据流集成分类的算法

针对数据流中概念漂移和噪声问题,提出一种新型的增量式学习的数据流集成分类算法。首先,引入噪声过滤机制过滤噪声;然后,引入假设检验方法对概念漂移进行检测,以增量式C4.5决策树为基分类器构建加权集成模型;最后,实现增量式学习实例并随之动态更新分类模型。实验结果表明,该集成分类器对概念漂移的检测精度达到95%~97%,对数据流抗噪性保持在90%以上。该算法分类精度较高,且在检测概念漂移的准确性和抗噪性方面有较好的表现。     

Dissimilar pixel counting based impulse detector for two-phase mixed noise removal

Mixed noise is a challenging noise model due to its statistical complexity. A new two-phase denoising method based on an impulse detector using dissimilar pixel counting is proposed in this paper. This method consists of two stages: detection and filtering. For the detection phase, average difference scheme is proposed to distinguish whether two neighboring pixels are similar or not, and then the number of dissimilar pixels is compared with a threshold to locate the outlier point in noisy image. An iterative framework is used for detection accuracy with the least numbers of iteration. For the filtering phase, an extended trilateral filter is used to remove the mixture of Gaussian and impulse noise, which are treated differently depending on the guidance matrix from the detection phase. Extensive experimental results demonstrate that the proposed method exhibits better noise detection capability and outperforms many existing two-phase mixed noise removal methods in both quantitative evaluation and visual quality.

     

Quantum and impulse noise filtering from breast mammogram images

Recent advances in the field of image processing have shown that level of noise highly affect the quality and accuracy of classification when working with mammographic images. In this paper, we have proposed a method that consists of two major modules: noise detection and noise filtering. For detection purpose, neural network is used which effectively detect the noise from highly corrupted images. Pixel values of the window and some other features are used as feature for the training of neural network. For noise removal, three filters are used. The weighted average value of these three filters is filled on noisy pixels. The proposed technique has been tested on salt & pepper and quantum noise present in mammogram images. Peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) are used for comparison of proposed technique with different existing techniques. Experiments shows that proposed technique produce better results as compare to existing methods.     

基于局部概率抽样的标签噪声过滤方法

分类学习任务中,在获取数据的过程中会不可避免地产生噪声,特别是标签噪声的存在不仅使得学习模型更复杂,而且容易造成过拟合并导致分类器泛化能力的下降。标签噪声过滤算法虽然在一定程度上可以解决上述问题,但是仍然存在噪声识别能力较差、分类效果不够理想以及过滤效率低等问题。针对这些问题,提出一种基于标签置信度分布的局部概率抽样方法来进行标签噪声过滤。首先利用随机森林分类器对样本的标签进行投票,从而获取每个样本的标签置信度;然后根据标签置信度的大小,将样本划分为易识别样本和难识别样本;最后分别采用不同的过滤策略对样本进行过滤。实验结果表明,在标签噪声存在的情况下,所提方法在大多数案例上能够保持较高的噪声识别能力,并且在分类泛化性能上也具有明显优势。     

Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition

The presence of noise in data is a common problem that produces several negative consequences in classification problems. In multi-class problems, these consequences are aggravated in terms of accuracy, building time, and complexity of the classifiers. In these cases, an interesting approach to reduce the effect of noise is to decompose the problem into several binary subproblems, reducing the complexity and, consequently, dividing the effects caused by noise into each of these subproblems. This paper analyzes the usage of decomposition strategies, and more specifically the One-vs-One scheme, to deal with noisy multi-class datasets. In order to investigate whether the decomposition is able to reduce the effect of noise or not, a large number of datasets are created introducing different levels and types of noise, as suggested in the literature. Several well-known classification algorithms, with or without decomposition, are trained on them in order to check when decomposition is advantageous. The results obtained show that methods using the One-vs-One strategy lead to better performances and more robust classifiers when dealing with noisy data, especially with the most disruptive noise schemes.     

递进的迭代脉冲噪声检测

目的 脉冲噪声是引起图像质量下降的主要原因,其滤除工作一直是图像处理领域的研究热点。对现行开关滤波算法在脉冲噪声检测时间、检测精准度和恢复策略上存在的问题进行理论分析,提出一种递进的迭代脉冲噪声检测算法(PIND),使噪声图像能够获得更好的恢复效果。方法 首先,采用具有全局统计意义的灰度直方图确定脉冲噪声与真实像素之间的灰度值的上边界和下边界,根据这个界线区分出疑似点和真实点;然后,利用具有局部结构意义的方法将噪声点从疑似点中寻找出来并判断噪声类型,存储在决策表G中;最后,根据决策表G中存储的噪声类型信息采用3种不同的的恢复策略滤除噪声。结果 对Lena、Peppers和Monkey 3幅具有代表性的图像增加不同密度和尺度的噪声进行对比实验,得出的数据表明,本文算法的脉冲噪声检测时间比现行两种经典算法提高520倍和15倍;检测精准度比现行经典开关滤波算法更加精准,准确率可以达到99%以上;恢复图像也具有更好的视觉效果和12 dB的峰值信噪比(PSNR)提升。结论 提出递进的迭代脉冲噪声检测算法能够在有效滤除脉冲噪声的同时,充分保护图像细节和恢复图像原有特征,并能够在噪声检测时间和精度以及峰值信噪比上弥补现行开关滤波算法的不足。     

一种有效去除脉冲噪声的新方法

提出了一种新的滤波方法。首先从原噪声图像和其中值滤波图像得到细节图像。通过使用一种新的噪声检测方法得到另一幅图像,使其只保留细节图像中的噪声。通过这个图像,可以更加准确地检测出污染图像中的噪声。对噪声图像中的每个像素,相应滤波器输出为原像素灰度值和窗中像素中值的线性组合。当前像素是一个脉冲的可能性越大,滤波过程中对它改变的就越多。与其它的中值类滤波方法相比,该方法不仅可以有效地去除噪声,而且更好地保留了图像细节。     

Analysis of classification accuracy for pre-filtered multichannel remote sensing data

Noise is one of the main factors degrading the quality of original multichannel remote sensing data and its presence influences classification efficiency, object detection, etc. Thus, pre-filtering is often used to remove noise and improve the solving of final tasks of multichannel remote sensing. Recent studies indicate that a classical model of additive noise is not adequate enough for images formed by modern multichannel sensors operating in visible and infrared bands. However, this fact is often ignored by researchers designing noise removal methods and algorithms. Because of this, we focus on the classification of multichannel remote sensing images in the case of signal-dependent noise present in component images. Three approaches to filtering of multichannel images for the considered noise model are analysed, all based on discrete cosine transform in blocks. The study is carried out not only in terms of conventional efficiency metrics used in filtering (MSE) but also in terms of multichannel data classification accuracy (probability of correct classification, confusion matrix). The proposed classification system combines the pre-processing stage where a DCT-based filter processes the blocks of the multichannel remote sensing image and the classification stage. Two modern classifiers are employed, radial basis function neural network and support vector machines. Simulations are carried out for three-channel image of Landsat TM sensor. Different cases of learning are considered: using noise-free samples of the test multichannel image, the noisy multichannel image and the pre-filtered one. It is shown that the use of the pre-filtered image for training produces better classification in comparison to the case of learning for the noisy image. It is demonstrated that the best results for both groups of quantitative criteria are provided if a proposed 3D discrete cosine transform filter equipped by variance stabilizing transform is applied. The classification results obtained for data pre-filtered in different ways are in agreement for both considered classifiers. Comparison of classifier performance is carried out as well. The radial basis neural network classifier is less sensitive to noise in original images, but after pre-filtering the performance of both classifiers is approximately the same.     

A multi-class classification strategy for Fisher scores: Application to signer independent sign language recognition

Fisher kernels combine the powers of discriminative and generative classifiers by mapping the variable-length sequences to a new fixed length feature space, called the Fisher score space. The mapping is based on a single generative model and the classifier is intrinsically binary. We propose a multi-class classification strategy that applies a multi-class classification on each Fisher score space and combines the decisions of multi-class classifiers. We experimentally show that the Fisher scores of one class provide discriminative information for the other classes as well. We compare several multi-class classification strategies for Fisher scores generated from the hidden Markov models of sign sequences. The proposed multi-class classification strategy increases the classification accuracy in comparison with the state of the art strategies based on combining binary classifiers. To reduce the computational complexity of the Fisher score extraction and the training phases, we also propose a score space selection method and show that, similar or even higher accuracies can be obtained by using only a subset of the score spaces. Based on the proposed score space selection method, a signer adaptation technique is also presented that does not require any re-training.     

椒盐噪声图像的非局部平均滤波算法

针对非局部平均(NLM)方法对椒盐噪声图像滤波效果较差的问题,通过引入噪声检测结果扩展NLM方法去除图像中椒盐噪声。在噪声检测阶段,利用图像的两个极值Lmin和Lmax把图像像素点分为非噪声点和噪声点。在滤波阶段,非噪声点的灰度值保持不变。对于噪声点,如果以该噪声点为中心的自适应滤波窗口内均为噪声点,则认为该噪声点位于图像自身灰度值为Lmin或Lmax的区域内,使用两个极值的统计结果进行恢复。否则,采用改进的NLM方法滤除噪声。构造联合噪声检测模板避免噪声点对相似权计算的干扰,噪声点的恢复值由非噪声点的灰度值加权平均得到。此外,采用迭代滤波策略对高密度噪声图像噪声点进行恢复。相关去噪实验结果证实了算法去噪的有效性,不足之处是算法的时间复杂度较高。     

Classifier ensembles to improve the robustness to noise of bearing fault diagnosis

In this paper, we perform a noise analysis to assess the degree of robustness to noise of a neural classifier aimed at performing multi-class diagnosis of rolling element bearings. We work on vibration signals collected by means of two accelerometers and we consider ten levels of noise, each of which characterized by a different signal-to-noise ratio ranging from 40.55 to ?11.35 db. We classify the noisy signals by means of a neural classifier initially trained on signals without noise and then we repeat the training process with signals affected by increasing levels of noise. We show that adding noisy signals to the training set we can significantly increase the classification accuracy of a single classifier. Finally, we apply the two most used strategies to combine classifiers: classifier fusion and classifier selection, and show that, in both cases, we can significantly increase the performance of the single best classifier. In particular, classifier selection achieves the best results for low and medium levels of noise, while classifier fusion is the most accurate for high levels of noise. The analysis presented in the paper can be profitably used to identify both the type of classifier (e.g., single classifier or classifier ensemble) and how many and which noise levels should be used in the training phase in order to achieve the desired classification accuracy in the application domain of interest.     

基于C4. 5和NB混合模型的数据流分类算法

具有概念漂移的含噪数据流的分类问题成为数据流挖掘领域研究的热点之一。提出了一种基于C4. 5和Naive I3ayes混合模型的数据流分类算法CDSMM。它以C4.5作为基分类器,采用朴素贝叶斯分类器过滤噪音,同时引入假设检验中的u检验方法检测概念漂移,动态更新模型。实验结果表明,CDSMM算法在处理带有噪音的概念漂移数据流时具有比同类算法更好的分类正确率。     

基于双阈值及迭代法的图像脉冲噪声检测算法

为了减少图像中的脉冲噪声对后续图像处理的影响,针对脉冲噪声的特点,提出了双阈值和迭代法的噪声检测算法。双阈值选取方法理论可靠,两次迭代保证了噪声点检测具有较高的正确率,最后的选择性中值滤波算法也使得图像的细节不被模糊。实验结果表明,所提算法具有较强的自适应性、较低的噪声漏检率以及较好的滤波效果。     

基于自适应噪声添加的防御对抗样本的算法

深度神经网络容易受到对抗样本的攻击。为了解决这个问题,一些工作通过向图像中添加高斯噪声来训练网络,从而提高网络防御对抗样本的能力,但是该方法在添加噪声时并没有考虑到神经网络对图像中不同区域的敏感性是不同的。针对这一问题,提出了梯度指导噪声添加的对抗训练算法。该算法在训练网络时,根据图像中不同区域的敏感性向其添加自适应噪声,在敏感性较大的区域上添加较大的噪声抑制网络对图像变化的敏感程度,在敏感性较小的区域上添加较小的噪声提高其分类精度。在Cifar-10数据集上与现有算法进行比较,实验结果表明,该方法有效地提高了神经网络在分类对抗样本时的准确率。     

Blind image noise level estimation using texture-based eigenvalue analysis

Blind noisy image estimation is useful in many visual processing systems. The challenge lies in accurately estimating the image noise level without any priori information of the image. To tackle this challenge, an iterative texture-based eigenvalue analysis approach is proposed in this paper. The proposed approach utilizes the eigenvalue analysis to mathematically derive a new noise level estimator based on weak-textured image patches. Furthermore, a new texture strength measure is proposed to adaptively select weak-textured patches from the noisy image. Experimental results are provided to demonstrate that the proposed image noise level estimation approach yields superior accuracy and stability performance to that of conventional noise level estimation approaches, so that to improve the performance of image denoising algorithm.     

脉冲噪声环境下基于卡尔曼滤波的语音增强

语音增强的目标在于从含噪信号中提取纯净语音,纯净语音在某些环境下会被脉冲噪声所污染,但脉冲噪声的时域分布特征却给语音增强带来困难,使传统方法在脉冲噪声环境下难以取得满意效果。为在平稳脉冲噪声环境下进行语音增强,提出了一种新方法。该方法通过计算确定脉冲噪声样本的能量与含噪信号样本的能量之比最大的频段,利用该频段能量分布情况逐帧判别语音信号是否被脉冲噪声所污染。进一步地,该方法只在被脉冲噪声污染的帧应用卡尔曼滤波算法去噪,并改进了传统算法执行时的自回归(AR)模型参数估计过程。实验中,采用白色脉冲噪声以及有色脉冲噪声污染语音信号,并对低输入信噪比的信号进行语音增强,结果表明所提出的算法能显著地改善信噪比和抑制脉冲噪声。     

基于动态策略的多源迁移学习数据流分类研究

为解决数据流分类中的概念漂移和噪声问题,提出一种基于样本确定性的多源迁移学习方法。该方法存储多源领域上由训练得到的分类器,求出各源领域分类器对目标领域数据块中每个样本的类别后验概率和样本确定性值。在此基础上,将样本确定性值满足当前阈值限制的源领域分类器与目标领域分类器进行在线集成,从而将多个源领域的知识迁移到目标领域。实验结果表明,该方法能够有效消除噪声数据流给不确定分类器带来的不利影响,与基于准确率选择集成的多源迁移学习方法相比,具有更高的分类准确率和抗噪稳定性。     

Ensemble-based noise detection: noise ranking and visual performance evaluation

Noise filtering is most frequently used in data preprocessing to improve the accuracy of induced classifiers. The focus of this work is different: we aim at detecting noisy instances for improved data understanding, data cleaning and outlier identification. The paper is composed of three parts. The first part presents an ensemble-based noise ranking methodology for explicit noise and outlier identification, named Noise- Rank, which was successfully applied to a real-life medical problem as proven in domain expert evaluation. The second part is concerned with quantitative performance evaluation of noise detection algorithms on data with randomly injected noise. A methodology for visual performance evaluation of noise detection algorithms in the precision-recall space, named Viper, is presented and compared to standard evaluation practice. The third part presents the implementation of the NoiseRank and Viper methodologies in a web-based platform for composition and execution of data mining workflows. This implementation allows public accessibility of the developed approaches, repeatability and sharing of the presented experiments as well as the inclusion of web services enabling to incorporate new noise detection algorithms into the proposed noise detection and performance evaluation workflows.     

基于信息熵和改进相似度协同过滤算法

为了减少协同过滤算法存在的噪音数据以及数据稀疏性问题,提高算法准确性,本文提出一种基于信息熵和改进相似度的协同过滤算法,使用用户信息熵模型来判断噪音数据,排除噪音数据对实验结果的干扰;使用面向稀疏数据的改进相似度计算方法,使用全部评分数据而不是依靠共同的评分项来计算,对缓解稀疏数据对推荐结果的精确性影响有很大帮助。实验结果表明,该算法能在一定程度上排除噪音数据对结果的影响,缓解数据稀疏对推荐结果精确性的干扰,提高该推荐算法的精确性,且缓解了传统推荐系统算法中常见的一些问题,与传统的协同过滤算法相比,该算法的精确性更高。     

随机噪声干扰下的迭代学习控制器设计

为解决迭代学习控制系统中随机噪声扰动问题,提出基于无限脉冲响应（ⅡR）数字滤波器的优化迭代学习控制器设计方法。该方法在首次迭代时对系统输出误差进行基于小波变换的两轮实验法滤波;其次根据小波滤波获得的输出误差确定部分及原误差信号作为输入输出辨识出等效ⅡR线性滤波器,并重构优化误差目标函数,进一步利用优化方法对迭代学习控制器优化设计;最后利用获得的线性滤波器及新学习律对系统进行后续批次迭代,直到满足收敛条件为止。仿真显示：在针对输出误差二范数这个性能指标,该方法与小波滤波相比,降低了近15%,并消除了由于小波滤波阈值选取过小产生的振铃现象;在批次间噪声累积上,降低了9%左右。仿真结果表明,提出的等效滤波器综合设计方法,有效抑制了随机噪声的影响,并提高了系统跟踪的准确性。