空-谱二维蚁群组合优化SVM的高光谱图像分类

提出了一种空-谱二维特征蚁群组合优化支持向量机的高光谱图像分类算法。利用两类蚁群分别在光谱维空间和样本分布空间交替搜索最大类间距波段组合和异质样本,提取最优特征波段,降低了高光谱的波段信息冗余,去除训练样本中的异质样本,优化了训练样本特征空间分布。将蚁群组合优化后的高光谱图像和训练样本应用到支持向量机(SVM)分类器中,扩大了特征空间类间距,提高了SVM算法的分类精度。实验表明该算法总分类精度达95.45%,Kappa系数0.925 2,是一种分类精度较高的高光谱图像分类方法。     

改进加权支持向量机回归方法器件易损性评估

加权支持向量机回归算法,几乎都是以样本输入空间中的一个重要特征量的函数来确定权值,造成了在高维特征空间中作回归可能存在较大误差。针对这一问题,提出利用高维特征空间中的欧基里德距离来确定权值的方法,构造了一种改进的加权支持向量机回归算法,并将其应用到电子器件高功率微波易损性评估中。仿真结果表明：该方法具有比模糊神经网络法、标准支持向量机回归算法和一般的加权支持向量机回归算法更高的预测精度。由于增加了权值的计算过程,相对于标准支持向量机回归和模糊神经网络方法,该方法的效率较低,但与一般的加权支持向量机回归算法相当。     

基于数据挖掘的本体构建与重构技术研究

针对目前本体构建与重构过程中数据处理效率低的问题,运用支持向量机分类及K-均值聚类的方法对本体构建数据进行处理,从文本数据中抽取关注的特定的信息,运用基于二叉树的多分类支持向量机以及支持向量机与K-均值融合的多样本聚类,总结基于分类与聚类的本体构建过程,并以离散型和连续型两种数据样本验证了方法的可行性。实验结果表明,基于数据挖掘的本体构建与重构技术具有良好的应用效果。     

基于多类支持向量机的恒星光谱分类方法

支持向量机作为一种经典的分类方法被广泛应用于恒星光谱分类领域。该方法在实际应用中取得了较为理想的分类效果,但其面临无法解决多分类问题的挑战。在支持向量机的基础上,提出多类支持向量机,建立基于多类支持向量机的恒星光谱分类模型。该方法的最大优势是经过一次分类过程,可以确定多类样本的类属。SDSS DR8恒星光谱数据上的比较实验表明,本研究所提的方法较之已有多分类方法在分类性能上有一定的提升。     

利用融合数据分布特征的模糊双支持向量机对恒星光谱分类

恒星光谱分类是天文学研究的一个热点问题。随着观测光谱数量的急剧增加,传统的人工分类无法满足实际需求,急需利用自动化技术,特别是数据挖掘算法来对恒星光谱进行自动分类。关联规则、神经网络、自组织网络等数据挖掘算法已广泛应用于恒星光谱分类。其中,支持向量机（SVM）分类能力突出,被广泛应用于恒星光谱分类。该方法试图在两类样本之间找到一个最优分类面将两类分开。该方法具有较高的时间复杂度,计算效率有限。双支持向量机（TWSVM）的出现有效地解决了SVM面临的效率问题。该方法通过构造两个非平行的分类面将两类分开,每一类靠近某个分类面,而远离另一个分类面。TWSVM的计算效率较之传统SVM提高近4倍,因此,自TWSVM提出后便受到研究人员的持续关注。但上述方法在分类决策时,一方面没有考虑数据的分布特征,另一方面较易受噪声点和奇异点的影响,分类效率难以显著提升。鉴于此,在双支持向量机的基础上,提出融合数据分布特征的模糊双支持向量机（TWSVM-SDP）。该方法引入线性判别分析（LDA）的类间离散度和类内离散度,用以表征光谱数据的分布性状;引入模糊隶属度函数用以降低噪声点和奇异点对分类结果的影响。在SDSS DR8恒星光谱数据集上的比较实验表明,与支持向量机SVM、双支持向量机TWSVM等传统分类方法相比,融合数据分布特征的模糊双支持向量机TWSVM-SDP具有更优的分类能力。该方法亦存在一定的局限性,其中一大难题是其无法处理海量光谱数据。接下来将利用大数据处理技术,来对所提方法在大数据环境下的适应性展开进一步研究。     

基于改进型人工鱼群算法的支持向量机参数优化

支持向量机在高维度、小样本情况下具有独特优势,但同时支持向量机的参数优化极大制约了其分类效果,目前参数优化缺乏系统的理论指导。针对传统DAG-SVM训练分类器较多,训练耗时长,分类效果受到结构排序的影响,提出了一种基于“1 vs R”策略的改进型算法;针对 SVM传统参数优化方式耗时大,优化精度不高,提出了改进型人工鱼群算法;最后结合1 vs R-DAG支持向量机算法与改进型人工鱼群算法,得到一种新的改进型支持向量机算法。仿真对比实验证实,对支持向量机的参数优化是有效可行的。     

基于双树复数小波变换的微钙化诊断方法

提出一种基于双树复数小波变换的微钙化分类方法.通过提取基于小波和灰度直方图的纹理特征,结合遗传算法进行特征优化,分别用神经网络,支持向量机和KNN分类器进行微钙化的良恶性分类.对三种不同的分类器进行对比,结果表明:KNN分类器取得最好的效果,而支持向量机优于神经网络.KNN分类器对比于神经网络和支持向量机,无需训练,可节约训练时间,最直接地利用了样本和样本之间的关系,减少了类别特征选择不当对分类结果造成的不利影响,可以最大程度地减少分类过程中的误差项.在类别决策时,KNN分类器只与极少量的相邻样本有关,可以较好地避免样本数量的不平衡问题.与传统的小波比较,双树复数小波具有近似平移不变性和正则性,对图像信号具有良好的方向选择性,且冗余度有限,计算量较小.     

基于双树复数小波变换的微钙化诊断方法

提出一种基于双树复数小波变换的微钙化分类方法.通过提取基于小波和灰度直方图的纹理特征,结合遗传算法进行特征优化,分别用神经网络,支持向量机和KNN分类器进行微钙化的良恶性分类.对三种不同的分类器进行对比,结果表明:KNN分类器取得最好的效果,而支持向量机优于神经网络.KNN分类器对比于神经网络和支持向量机,无需训练,可节约训练时间,最直接地利用了样本和样本之间的关系,减少了类别特征选择不当对分类结果造成的不利影响,可以最大程度地减少分类过程中的误差项.在类别决策时,KNN分类器只与极少量的相邻样本有关,可以较好地避免样本数量的不平衡问题.与传统的小波比较,双树复数小波具有近似平移不变性和正则性,对图像信号具有良好的方向选择性,且冗余度有限,计算量较小.     

基于集成最小二乘支持向量机方法的近红外光谱分析

针对近红外（Near Infrared,NIR）光谱测量中的小样本问题。本文提出了一种集成最小二乘支持向量机（Ensemble Least Squares Support Vector Machine,ELS-SVM）新算法。首先使用随机子空间算法（Random Subspace Method,RSM）原始高维变量空间划分为若干个低维度的子空间,然后分别在各个子空间建立最小二乘支持向量机（LS-SVM）模型,最后构造一个集成结果来进行预测。针对一批柴油样本的实验结果表明,本法对柴油十六烷值的预测精度优于传统的LS-SVM方法。     

基于模糊支持向量机的高光谱图像分类

常规支持向量机应用到高光谱图像分类中有较好的分类效果,但它对训练样本内部的噪声和孤立点特别敏感,在一定程度上影响了支持向量机的分类性能,针对该问题,引入了模糊支持向量机(FSVM),并且利用灰色关联分析代替模糊隶属度的求解,将这种基于灰色关联分析的模糊支持向量机与一对多算法相结合,解决了多类高光谱图像分类问题。HYDICE高光谱图像分类结果表明,噪声和孤立点训练样本对支持向量机的影响得到了有效地抑制,相比于常规支持向量机方法,分类精度得到了明显的提高。     

基于太赫兹光谱技术的贝母品种鉴别方法研究

贝母是广泛应用于临床实践的中药材,其中川贝母尤为珍贵,存在掺假及伪冒现象,伪劣贝母会对用药者的健康产生不良影响。太赫兹时域光谱（Terahertz time domain spectroscopy）具有瞬态性、宽带性、安全性和穿透性等许多优越特性,近年来在药食无损检测领域十分活跃。以四种常见贝母（川贝母、平贝母、伊贝母、浙贝母）为研究对象,探究利用太赫兹时域光谱技术鉴别贝母品种的可行性。利用TAS7500TS太赫兹光谱系统采集贝母样品在0.6～3.0 THz范围内的光谱,并结合化学计量学方法进行预处理与建立分类模型。当分类数量为二时,称为二分类问题,当分类数量超过二时称为多分类问题。利用偏最小二乘判别分析（PLS-DA）建立四种贝母的二分类模型;使用Savitzky-Golay（S-G）平滑、多元散射校正（MSC）、标准正态变量变换（SNV）、移动平均、基线偏移校正（Baseline offset）对原始光谱进行预处理,再采用主成分分析（PCA）对预处理后的数据进行降维,以减少数据运算量、简化运算,最后建立随机森林（RF）、支持向量机（SVM）、反向传播神经网络（BPNN）多分类模型。结果显示：川-伊贝母二分类鉴别模型正确率为93.333%,平-浙贝母二分类鉴别模型正确率为98.333%,其他四种二分类鉴别模型正确率均为100%。对建立的多分类模型进行对比分析发现SVM结合SNV建模效果最好,其中川贝母正确率为95.349%,伊贝母正确率为96.552%,平贝母与浙贝母正确率均为100%,整体正确率高达97.490%。研究结果表明利用太赫兹时域光谱技术鉴别不同品种贝母是可行的,并建立了分类效果较好的SNV-SVM多分类模型,为把控中药材质量提供一种新的手段,对维护中药材市场的正常运转具有重要的意义。     

基于Logistic回归模型的Blazar天体的分类

从文献中收集了205个Blazar天体,包括142个BL Lac天体和63个平谱射电类星体(FSRQs).对这些天体的类别与它们的红移、射电5 GHz辐射流量、光学V波段流量、1 keV处X射线流量、X射线光子谱指数进行了相关性和Logistic回归分析.结果表明,对Blazar天体分类产生主要影响的因素是红移、射电5 GHz辐射流量和X射线光子谱指数,综合应用这三个物理量判别Blazar天体的分类的准确率可达到91.2%,得到的分类方程具有良好的预测效果,可以作为Blazar天体分类的一个重要的判据.而光学V波段流量和1 keV处X射线流量不能区分开BL Lac天体和FSRQs,它们与Blazar天体分类没有相关性.本文结果支持将BL Lac天体和FSRQs归为Blazar天体,不同类别的Blazar天体之间能通过一种演化序列相联系. 关键词： Logistic 分类 Blazar天体     

Experimental Study and Comparison of Imbalance Ensemble Classifiers with Dynamic Selection Strategy

Imbalance ensemble classification is one of the most essential and practical strategies for improving decision performance in data analysis. There is a growing body of literature about ensemble techniques for imbalance learning in recent years, the various extensions of imbalanced classification methods were established from different points of view. The present study is initiated in an attempt to review the state-of-the-art ensemble classification algorithms for dealing with imbalanced datasets, offering a comprehensive analysis for incorporating the dynamic selection of base classifiers in classification. By conducting 14 existing ensemble algorithms incorporating a dynamic selection on 56 datasets, the experimental results reveal that the classical algorithm with a dynamic selection strategy deliver a practical way to improve the classification performance for both a binary class and multi-class imbalanced datasets. In addition, by combining patch learning with a dynamic selection ensemble classification, a patch-ensemble classification method is designed, which utilizes the misclassified samples to train patch classifiers for increasing the diversity of base classifiers. The experiments’ results indicate that the designed method has a certain potential for the performance of multi-class imbalanced classification.     

Large-Scale Location-Aware Services in Access: Hierarchical Building/Floor Classification and Location Estimation Using Wi-Fi Fingerprinting Based on Deep Neural Networks

We report the results of our investigation on the use of deep neural networks (DNNs) for building/floor classification and floor-level location estimation based on Wi-Fi fingerprinting. We propose a new DNN architecture based on a stacked autoencoder for feature space dimension reduction and a feed-forward classifier for multi-label classification with arg max functions to convert multi-label classification results into multi-class classification ones. We also demonstrate a prototype system for floor-level location estimation using received signal strengths measured on XJTLU campus. Our results show the strengths of DNN-based approaches, providing near state-of-the-art performance with less parameter tuning and higher scalability.     

基于Bayes决策的光谱分类

天文观测技术的迅速发展推动了大规模的星系光谱巡天计划如SDSS、LAMOST等,面对这些巡天项目所观测到的海量光谱数据, 研究自动的光谱分析方法已成为必然的选择。研究了基于Bayes决策的光谱分类方法,将光谱分为恒星,星系和类星体三类。首先采用主分量分析来进行特征提取,将光谱投影到由三个主分量构成的特征空间中; 然后,采用非参数密度估计Parzen窗法来估计类条件概率密度函数; 最后利用基于最小错误率的Bayes决策进行分类。在Parzen窗法中,核宽很大程度上影响着估计效果,从而影响着分类效果。通过详尽的实验分析了核宽和分类效果的关系,发现当核宽接近某个阈值时,识别率将会增加,但小于这个阈值时,识别率反而下降。     

Binary Classification Quantum Neural Network Model Based on Optimized Grover Algorithm

We focus on the problem that the Grover algorithm is not suitable for the completely unknown proportion of target solutions. Considering whether the existing quantum classifier used by the current quantum neural network (QNN) to complete the classification task can solve the problem of the classical classifier, this paper proposes a binary quantum neural network classifical model based on an optimized Grover algorithm based on partial diffusion. Trial and error is adopted to extend the partial diffusion quantum search algorithm with the known proportion of target solutions to the unknown state, and to apply the characteristics of the supervised learning of the quantum neural network to binary classify the classified data. Experiments show that the proposed method can effectively retrieve quantum states with similar features. The test accuracy of BQM retrieval under the depolarization noise at the 20th period can reach 97% when the depolarization rate is 0.1. It improves the retrieval accuracy by about 4% and 10% compared with MSE and BCE in the same environment.     

基于表面增强拉曼光谱的毒品鉴别方法

毒品的快速检测在抑制毒品的传播,打击毒品犯罪方面有着举足轻重的作用.表面增强拉曼光谱(SERS)技术具有指纹识别、检测速度快、样品用量少、无损伤等众多优点而受到了关注,其特点特别适合于公安机关现场快速检测执法.本文利用金纳米粒子溶胶作为增强试剂对拉曼光谱进行增强,制作1μg·mL-1的苯丙胺、氯胺酮、芬太尼、海洛因、可...     

基于面向对象分类约束的融合方法研究

像素级融合方法中常出现色彩突变或色彩失真现象。通过分类信息对融合进行约束可以部分消除目标地物边界的这些现象。然而传统的基于像素分类的影像融合方法由于分类中的“椒盐效应”,导致融合效果受到一定的影响和限制。采用面向对象分类约束的方法对该融合方法进行改进。首先采用面向对象分类方法进行影像分类,解决了基于像素分类中的“椒盐效应”问题;其次将分类结果作为影像融合的约束条件,利用色度饱和度明度(HSV)变换进行融合;最后将该方法的结果与多种融合方法的结果进行定量比较,发现该方法除在目视上取得很好的增强效果外,在信息熵、方差等指标上也取得了很好的效果。     

Preference-Driven Classification Measure

Classification is one of the main problems of machine learning, and assessing the quality of classification is one of the most topical tasks, all the more difficult as it depends on many factors. Many different measures have been proposed to assess the quality of the classification, often depending on the application of a specific classifier. However, in most cases, these measures are focused on binary classification, and for the problem of many decision classes, they are significantly simplified. Due to the increasing scope of classification applications, there is a growing need to select a classifier appropriate to the situation, including more complex data sets with multiple decision classes. This paper aims to propose a new measure of classifier quality assessment (called the preference-driven measure, abbreviated p-d), regardless of the number of classes, with the possibility of establishing the relative importance of each class. Furthermore, we propose a solution in which the classifier’s assessment can be adapted to the analyzed problem using a vector of preferences. To visualize the operation of the proposed measure, we present it first on an example involving two decision classes and then test its operation on real, multi-class data sets. Additionally, in this case, we demonstrate how to adjust the assessment to the user’s preferences. The results obtained allow us to confirm that the use of a preference-driven measure indicates that other classifiers are better to use according to preferences, particularly as opposed to the classical measures of classification quality assessment.     

Research on classification of LiDAR images derived from waveform decomposition over a suburban area

Light detection and ranging (LiDAR), as an active remote sensing technology, is characterized by providing high-precision geographical location information. In this study, we further explored its capability in image classification over a suburban area. Firstly, full waveforms of small footprint airborne LiDAR were decomposed into discrete point clouds. During the decomposition, six parameters describing the physical interaction between laser pulse and the targets were calculated. They are amplitude, pulse width, central position, range, backscatter cross-section and backscatter coefficient. Secondly, the point clouds were interpolated into raster. Correspondingly, six high spatial resolution images (0.5 m) were produced. Three classification models namely decision tree (DT), maximum likelihood (ML) and support vector machine (SVM) were established based on these images. The objects of interest were classified into buildings, trees, bare soil and crop land. Results showed that all these three models yielded high overall accuracy and kappa coefficient. SVM performed the best with the highest overall accuracy (87.85%) and kappa coefficient (83.29%). Therefore, we came to conclude that classification models can also achieve satisfactory classification accuracy on LiDAR images as they did on common remote-sensed images. In addition, our study proved that physical information derived from waveform LiDAR showed good potential in classification.