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Co-Occurrence Histogram Based Ensemble of Classifiers for Classification of Cervical Cancer Cells
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To explore the potential of conventional image processing techniques in the classification of cervical cancer cells, in this work, a co-occurrence histogram method was employed for image feature extraction and an ensemble classifier was developed by combining the base classifiers, namely, the artificial neural network (ANN), random forest (RF), and support vector machine (SVM), for image classification. The segmented pap-smear cell image dataset was constructed by the k-means clustering technique and used to evaluate the performance of the ensemble classifier which was formed by the combination of above considered base classifiers. The result was also compared with that achieved by the individual base classifiers as well as that trained with color, texture, and shape features. The maximum average classification accuracy of 93.44% was obtained when the ensemble classifier was applied and trained with co-occurrence histogram features, which indicates that the ensemble classifier trained with co-occurrence histogram features is more suitable and advantageous for the classification of cervical cancer cells. 相似文献
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大规模的netflow训练数据集是构建高质量、高稳定网络流量分类器的必然要求。但随着网络流特征维数的提高和数据集规模的扩大,无论是网络流的分析处理还是基于支持向量机(SVM)的分类器模型的训练,都无法在有效的时间内得到有效的处理结果。本文基于Hadoop云计算平台,采用MapReduce技术对SVM网络流量分类器进行分布式学习和训练,构建CloudSVM网络流量分类器。通过对来自校园网出口镜像的近2 T的大规模网络流量的跟踪文件的分布式存储和处理,对抽取的样本数据集进行分类,实验验证了基于Hadoop平台分布式存储和并行处理大规模网络数据集的高效率性,也验证了CloudSVM分类器在不降低分类准确度的情况下可以快速收敛到最佳,并随着大规模网络流样本的增加,SVM分类器训练的时间趋近平稳。 相似文献
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针对人脸识别技术易受光照、姿态、表情等影响 ,为了增强人脸识别算法的鲁棒性,提出首先采用 LBP算法提取人脸图像的局部纹理特征,使用PCA算法将高维的空间人脸图像投影到低维的 特征空间,使 用LDA算法利用人脸类别标签信息寻找最优的投影向量,实现了人脸图像维度进一步地压缩 ,最后使用SVM 分类器分类匹配得到识别结果。分别使用ORL和Yale人脸数据库验证了算法的有效性,实 验结果表明,文 中该方法具有良好的识别性能,与其它的识别算法相比,识别率有了较大的提高。 相似文献
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提出了一种基于支撑向量机的多类分类器,用N-1个支撑向量机组合构成一个具有二叉树结构形式的N-多类分类器.讨论了该多类分类器的泛化推广能力,同时还提出了该多类分类器的基于特征空间的BTSVM学习算法,BTSVM算法使用核函数转换的方式计算特征空间的样本距离;采用类间最小距离最大化作为聚类准则,在每个决策结点产生两个最优子集;然后采用支撑向量机学习算法学习两个最优子集,确定决策结点的最优分类面.理论和实验结果表明,本文提出的基于支撑向量机的多类分类器在整体性能上要优于其它类似的分类器系统。 相似文献
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Tristan Groléat Sandrine Vaton Matthieu Arzel 《International Journal of Network Management》2014,24(4):253-271
Analyzing the composition of Internet traffic has many applications nowadays, like tracking bandwidth‐consuming applications, QoS‐based traffic engineering and lawful interception of illegal traffic. Even though many flow‐based classification methods, such as support vector machines (SVM), have demonstrated their accuracy, few practical implementations of lightweight classifiers exist. We consider in this paper the design of a real‐time SVM traffic classifier at hundreds of Gb/s to allow online detection of categories of applications. We also implement a high‐speed flow reconstruction algorithm able to handle one million concurrent flows. The solution is based on the massive parallelism and low‐level network interface access of FPGA boards. We find maximum supported bit rates up to 408 Gb/s for classification and up to 20 GB/s for flow reconstruction for the most challenging trace. Results are confirmed using a commercial Combov2 board with a Virtex 5 FPGA. Copyright © 2014 John Wiley & Sons, Ltd. 相似文献
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天地一体化网络处在开放的电磁环境中,会时常遭受恶意网络入侵。为解决网络中绕过安全机制的非授权行为对系统进行攻击的问题,提出一种改进的遗传算法。该算法以决策树算法为适应度函数,通过删除数据集中的冗余特征,显著提高了对网络攻击的拦截率。通过机器学习进行异常分类,并利用遗传算法的特征选择功能,增强机器学习方法的分类效率。为验证算法的有效性,选用UNSW_NB15和UGRansome1819数据集进行训练和检测。使用随机森林、人工神经网络、K近邻和支持向量机等4种机器学习分类器进行评估,采用准确性、F1分数、召回率和混淆矩阵等指标评估算法的性能。实验证明,遗传算法作为特征选择工具能够显著提高分类准确性,并在算法性能上取得显著改善。同时,为解决弱分类器的不稳定性,提出一种集成学习优化技术,将弱分类器和强分类器集成进行优化。实验证实了该优化算法在提高弱分类器稳定性方面性能卓越。 相似文献
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利用CSK算法从图像碎片中提取运动目标的一个最小二乘分类,引入多通道颜色特征标定运动目标,通过当前图片碎片中的核函数周期性假设循环结构,一定程度拟补CSK算法使用目标灰度特征描述能力的不足。采用PCA降低特征维度并去除特征冗余信息,提高分类器参数更新速度,解决了CSK算法分类器参数更新线性化、无法适应目标发生较大变化时的运动目标跟踪问题。利用benchmark测试平台的算法集与测试数据集进行了实验,目标颜色核相关跟踪算法(TCKCT)的实验结果表明,对光照变化、背景杂乱、目标形变、目标运动速度较快、目标运动幅度较大的情况下,算法都有较好的跟踪效果。无人机跟踪遥控小车的物理实验结果,进一步验证了TCKCT算法特性,良好的实时性能够满足无人机目标跟踪要求,具有良好的实际应用前景。 相似文献
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多维贝叶斯分类器是处理多维分类问题的概率图形模型,其中属性变量可决定一个或多个类变量。文中针对属性变量维数较高和信息冗余问题,采用Fast ICA算法对属性变量进行降维,从而将高维属性变量约减为能较完整描述数据信息的低维属性变量。然后根据约减后的属性变量构建多维贝叶斯分类器;最终,通过理论分析得到基于ICA的多维贝叶斯分类器的性能较好。实验结果表明,对3组基准数据集的分类,基于ICA的多维贝叶斯分类器相比于其他算法具有较高的分类准确率。 相似文献
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Davide Tammaro Silvio Valenti Dario Rossi Antonio Pescapé 《International Journal of Network Management》2012,22(6):451-476
The use of packet sampling for traffic measurement has become mandatory for network operators to cope with the huge amount of data transmitted in today's networks, powered by increasingly faster transmission technologies. Therefore, many networking tasks must already deal with such reduced data, more available but less rich in information. In this work we assess the impact of packet sampling on various network monitoring‐activities, with a particular focus on traffic characterization and classification. We process an extremely heterogeneous dataset composed of four packet‐level traces (representative of different access technologies and operational environments) with a traffic monitor able to apply different sampling policies and rates to the traffic and extract several features both in aggregated and per‐flow fashion, providing empirical evidences of the impact of packet sampling on both traffic measurement and traffic classification. First, we analyze feature distortion, quantified by means of two statistical metrics: most features appear already deteriorated under low sampling step, no matter the sampling policy, while only a few remain consistent under harsh sampling conditions, which may even cause some artifacts, undermining the correctness of measurements. Second, we evaluate the performance of traffic classification under sampling. The information content of features, even though deteriorated, still allows a good classification accuracy, provided that the classifier is trained with data obtained at the same sampling rate of the target data. The accuracy is also due to a thoughtful choice of a smart sampling policy which biases the sampling towards packets carrying the most useful information. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
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支持向量机(support vector machine,SVM)是一类具有良好泛化能力的机器学习算法,适合应用于互联网动态环境下的流量分类问题。目前将SVM扩展到流量分类这样的多分类问题的方法主要有One-Against-All和One-Against-One方法。这些方法都基于单一的特征空间训练SVM两分类器,没有考虑到不同特征对不同流量类的不同区分能力,因此获得的分离超平面并不是最合理的。为此提出了可变特征空间的SVM集成方法,即为每个两分类 SVM 构建具有最优区分能力的独立特征空间,单独训练两分类 SVM,最后再利用One-Against-All和One-Against-One方法集成为多分类器。实验表明,与原来的单一特征空间的One-Against-All和One-Against-One集成方法相比,提出的方法能有效提高流量分类器分类精度和召回率,更易获得最优分离超平面。 相似文献
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Support vector machine-based classification scheme for myoelectric control applied to upper limb 总被引:1,自引:0,他引:1
This paper proposes and evaluates the application of support vector machine (SVM) to classify upper limb motions using myoelectric signals. It explores the optimum configuration of SVM-based myoelectric control, by suggesting an advantageous data segmentation technique, feature set, model selection approach for SVM, and postprocessing methods. This work presents a method to adjust SVM parameters before classification, and examines overlapped segmentation and majority voting as two techniques to improve controller performance. A SVM, as the core of classification in myoelectric control, is compared with two commonly used classifiers: linear discriminant analysis (LDA) and multilayer perceptron (MLP) neural networks. It demonstrates exceptional accuracy, robust performance, and low computational load. The entropy of the output of the classifier is also examined as an online index to evaluate the correctness of classification; this can be used by online training for long-term myoelectric control operations. 相似文献
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一种基于半监督学习的应用层流量分类方法 总被引:3,自引:0,他引:3
基于应用层的流量分类在用户行为识别、网络带宽管理等方面有着十分重要的应用.将机器学习应用到应用层流量分类问题中.首先提出了一种基于熵函数的组合式特征选择算法,提取了5种TCP连接的特征.针对监督学习中无法识别新流量类型的问题,提出了一种基于半监督学习的流量分类算法.实验结果表明,算法的检测率优于Kmeans方法.在少量标记样本的情况下,随着未标记样本数增加,算法的检测率在增加. 相似文献
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Face recognition has been a hot-topic in the field of pattern recognition where feature extraction and classification play an important role. However, convolutional neural network (CNN) and local binary pattern (LBP) can only extract single features of facial images, and fail to select the optimal classifier. To deal with the problem of classifier parameter optimization, two structures based on the support vector machine (SVM) optimized by artificial bee colony (ABC) algorithm are proposed to classify CNN and LBP features separately. In order to solve the single feature problem, a fusion system based on CNN and LBP features is proposed. The facial features can be better represented by extracting and fusing the global and local information of face images. We achieve the goal by fusing the outputs of feature classifiers. Explicit experimental results on Olivetti Research Laboratory (ORL) and face recognition technology (FERET) databases show the superiority of proposed approaches. 相似文献