Cost-sensitive decision tree ensembles for effective imbalanced classification

Real-life datasets are often imbalanced, that is, there are significantly more training samples available for some classes than for others, and consequently the conventional aim of reducing overall classification accuracy is not appropriate when dealing with such problems. Various approaches have been introduced in the literature to deal with imbalanced datasets, and are typically based on oversampling, undersampling or cost-sensitive classification. In this paper, we introduce an effective ensemble of cost-sensitive decision trees for imbalanced classification. Base classifiers are constructed according to a given cost matrix, but are trained on random feature subspaces to ensure sufficient diversity of the ensemble members. We employ an evolutionary algorithm for simultaneous classifier selection and assignment of committee member weights for the fusion process. Our proposed algorithm is evaluated on a variety of benchmark datasets, and is confirmed to lead to improved recognition of the minority class, to be capable of outperforming other state-of-the-art algorithms, and hence to represent a useful and effective approach for dealing with imbalanced datasets.     

基于全局和局部特征的目标识别研究

为提高目标识别的准确性和快速性,提出了基于全局和局部特征对目标识别的方法。基于颜色直方图提取全局颜色特征,利用多尺度空间来表达目标的局部特征,最后将全局和局部特征进行数据融合得到图像的识别结果。实验结果表明,该方法很好地结合了目标的整体和局部信息,能有效地识别目标,且识别效果优于单一的全局特征和局部特征的识别效果。     

“软件人”感知系统的协同分类模型研究

“软件人”是计算机网络世界中的一类软件人工生命，是一种网络中的“虚拟机器人”，它具有拟人结构。作为对人的模拟，其感知系统应该像人的感知系统一样，具有区分感知对象的能力。基于人体感知系统的启发，运用大系统分析的“分解一集结”方法，本文提出了一种“软件人”感知系统的协同分类模型。该模型通过多分类器系统模拟人的不同感觉，从而实现“软件人”对感知对象的多感觉协同分类。仿真实验结果初步验证了本文提出模型的可行性。今后的工作是研究如何构造实际系统并将其应用于数字气田建设的数据处理工作中。     

Practical considerations of global and local variables

Fisher advocates that all variables should be made global to programs and subprograms in order to increase the maintainability and to reduce the problems with subprogram linkages, among other things. The fallacies of Fisher's arguments are shown and the advantages and necessity of using local variables are discussed. It is argued that, from a practical point of view, the use of local variables improves the comprehensibility and the time and space efficiency of programs, as well as makes correctness proofs easier and recursion possible.     

二维形状特征描述及分类识别研究进展综述

二维形状分类识别是计算机视觉和模式识别等领域的一个重要问题,在目标识别、图像理解等应用中发挥着重要作用.针对二维形状分类识别研究,主要从特征描述、形状分类识别、形状标准数据库三个方面综述了该方向近年来最新的研究工作.综合分析了二维形状特征表示方法,主要包括基于轮廓的、基于区域的、基于骨架的以及基于多特征融合的方法,并简...     

基于多分类器融合的步态识别方法

为了提高现有基于智能手机加速度传感器步态身份识别的性能,提出了一种基于多分类器融合（MCF）的识别方法。首先,针对现有方法所提取的步态特征较为单一的问题,对单个步态周期提取相对匀变加速度的速度变化量,以及单位时间内加速度变化量作为两类新特征（共16个）;其次,将新特征结合常用的时域、频域特征组成新的特征集,用于训练识别效果与训练时间俱佳的多个分类器;最后,采用多尺度投票法（MSV）对多分类器的输出进行融合处理,得到最终的分类结果。为了检测该方法的性能,采集了32个志愿者的步态数据。实验结果表明,新特征对于单个分类器的识别率平均提升5.95个百分点,最终通过MSV融合算法的识别率为97.78%。     

A study on the performances of dynamic classifier selection based on local accuracy estimation

Dynamic classifier selection (DCS) plays a strategic role in the field of multiple classifier systems (MCS). This paper proposes a study on the performances of DCS by Local Accuracy estimation (DCS-LA). To this end, upper bounds against which the performances can be evaluated are proposed. The experimental results on five datasets clearly show the effectiveness of the selection methods based on local accuracy estimates.     

Age estimation using a hierarchical classifier based on global and local facial features

The research related to age estimation using face images has become increasingly important, due to the fact it has a variety of potentially useful applications. An age estimation system is generally composed of aging feature extraction and feature classification; both of which are important in order to improve the performance. For the aging feature extraction, the hybrid features, which are a combination of global and local features, have received a great deal of attention, because this method can compensate for defects found in individual global and local features. As for feature classification, the hierarchical classifier, which is composed of an age group classification (e.g. the class of less than 20 years old, the class of 20-39 years old, etc.) and a detailed age estimation (e.g. 17, 23 years old, etc.), provide a much better performance than other methods. However, both the hybrid features and hierarchical classifier methods have only been studied independently and no research combining them has yet been conducted in the previous works. Consequently, we propose a new age estimation method using a hierarchical classifier method based on both global and local facial features. Our research is novel in the following three ways, compared to the previous works. Firstly, age estimation accuracy is greatly improved through a combination of the proposed hybrid features and the hierarchical classifier. Secondly, new local feature extraction methods are proposed in order to improve the performance of the hybrid features. The wrinkle feature is extracted using a set of region specific Gabor filters, each of which is designed based on the regional direction of the wrinkles, and the skin feature is extracted using a local binary pattern (LBP), capable of extracting the detailed textures of skin. Thirdly, the improved hierarchical classifier is based on a support vector machine (SVM) and a support vector regression (SVR). To reduce the error propagation of the hierarchical classifier, each age group classifier is designed so that the age range to be estimated is overlapped by consideration of false acceptance error (FAE) and false rejection error (FRE) of each classifier. The experimental results showed that the performance of the proposed method was superior to that of the previous methods when using the BERC, PAL and FG-Net aging databases.     

Remark on local and global stabilization of homogeneous bilinear systems

In this paper, we deal with the problem of stabilization of homogeneous bilinear systems. The aim is to clarify some results on stabilizability of these systems.     

基于分类器联合的联机图形识别方法研究

研究基于不同模式特征的多分类器联合问题，提出一种新的分类器联合方法，将该方法应用于联机几何图形的识别，实验中联合了3种分类器，每种分类器的机制各不相同，并且都基于不同的模式特征，将该分类器联合方法与现有的几种联合方法进行实验比较，实验结果表明该方法具有较高的识别率。     

基于粒子滤波与局部全局一致性学习的目标跟踪算法

针对目标形变及复杂背景条件下的目标跟踪问题,利用基于图的半监督学习方法,结合粒子滤波,提出一种自适应的目标跟踪算法。该算法利用局部全局一致性学习（LLGC）算法建立代价函数,将当前的候选状态作为未标记样本,以所有样本为顶点建立图,以代价函数的最优解作为当前的状态,从而得到当前帧的目标位置;同时利用跟踪结果对标记样本进行实时更新,以适应目标形变,部分遮挡以及环境光照的变化。实验结果表明,该方法能够很好地处理目标跟踪中常见的遮挡、相似背景干扰等复杂情形,实现对目标的鲁棒跟踪     

基于频带特征融合的GL-CNN遥感图像场景分类

高分辨率卫星遥感图像场景信息的分类对影像分析和解译具有重要意义，传统的高分辨卫星遥感图像场景分类方法主要依赖于人工提取的中、低层特征且不能很好的利用图像丰富的场景信息，针对这一问题，提出一种基于频带特征融合与GL-CNN(Guided Learning Convolutional Neural Network，指导学习卷积神经网络)的分类方法。首先通过NSWT（Non-Subsampled Wavelet Transform，非下采样小波变换）提取出图像的高低频子带，将高频子带进行频带特征融合得到融合高频子带，然后联合频谱角向能量分布曲线的平稳区间分析实现融合高频子带与低频子带的样本融合，最后指导卷积神经网络自动提取图像的高低频子带包含的高层特征来实现场景分类。通过对UCM_LandUse 21类数据进行试验表明，本文方法的分类正确率达到94.52%，相比以往算法有显著提高。     

基于频带特征融合的GL-CNN遥感图像场景分类

高分辨率卫星遥感图像场景信息的分类对影像分析和解译具有重要意义,传统的高分辨卫星遥感图像场景分类方法主要依赖于人工提取的中、低层特征且不能很好的利用图像丰富的场景信息,针对这一问题,提出一种基于频带特征融合与GL-CNN(Guided Learning Convolutional Neural Network,指导学习卷积神经网络)的分类方法。首先通过NSWT（Non-Subsampled Wavelet Transform,非下采样小波变换）提取出图像的高低频子带,将高频子带进行频带特征融合得到融合高频子带,然后联合频谱角向能量分布曲线的平稳区间分析实现融合高频子带与低频子带的样本融合,最后指导卷积神经网络自动提取图像的高低频子带包含的高层特征来实现场景分类。通过对UCM_LandUse 21类数据进行试验表明,本文方法的分类正确率达到94.52%,相比以往算法有显著提高。     

全局和局部特征相融合的人脸识别算法

为了进一步提高特征提取效率和人脸识别正确率,提出一种融合全局和局部特征的人脸识别算法。引入局部散度矩阵和全局散度矩阵,两者分别表征样本的全局特征和局部特征;基于同类样本尽可能的紧密而异类样本尽可能远离的事实,构造最优化问题,采用支持向量机建立人脸分类器,并通过仿真实验测试算法的性能。仿真结果表明,该算法不仅提高了人脸识别正确率,而且提高了人脸识别效率。     

基于深度学习的主轴承盖分类识别算法

机械零件自动分类识别算法,在智能工业、自动化加工等领域具有广阔地应用前景.针对汽车发动机主轴承盖零件自动分类时,存在特征多表面分布和光照敏感等难点问题,提出多分支特征融合卷积神经网络(MFF-CNN).MFF-CNN具有2个子网络分支,分别提取主轴承盖2个表面的特征,经过特征融合,形成最终的零件分类特征.在网络结构设计...     

基于分段降维和路径修正DTW的时序特征分类器设计

针对传统的动态时间弯曲（DTW）度量方法易出现过度弯曲现象且计算复杂度高、算法效率低等问题,提出一种基于路径修正的动态时间弯曲（UDTW）度量方法。首先通过分段降维方法——分段局部最大值平滑法（PLM）有效提取序列特征信息,减少UDTW的计算代价;其次,考虑了时间序列形态特征的相似性要求,给过度弯曲路径设置动态惩罚系数,以此修正路径的弯曲程度;最后,在改进度量距离基础上,采用1-近邻分类算法对时序数据进行分类,以提高时间序列相似性度量的准确率和效率。实验结果表明,在15个UCR数据集上,UDTW度量方法与传统DTW度量方法相比具有更高的分类准确率,UDTW在其中3个数据集上能实现100%分类正确;与导数DTW（DDTW）度量方法相比,UDTW分类准确率最多提高了71.8%,而PLM-UDTW在不影响分类准确率的前提下执行时间减小了99%。     

A measure of competence based on random classification for dynamic ensemble selection

In this paper, a measure of competence based on random classification (MCR) for classifier ensembles is presented. The measure selects dynamically (i.e. for each test example) a subset of classifiers from the ensemble that perform better than a random classifier. Therefore, weak (incompetent) classifiers that would adversely affect the performance of a classification system are eliminated. When all classifiers in the ensemble are evaluated as incompetent, the classification accuracy of the system can be increased by using the random classifier instead. Theoretical justification for using the measure with the majority voting rule is given. Two MCR based systems were developed and their performance was compared against six multiple classifier systems using data sets taken from the UCI Machine Learning Repository and Ludmila Kuncheva Collection. The systems developed had typically the highest classification accuracies regardless of the ensemble type used (homogeneous or heterogeneous).     

基于Multi-Agent的乳腺钼靶图像肿块分类方法

钼靶摄影是目前使用最广泛的乳腺癌早期诊断技术,恶性肿块是钼靶图像中乳腺癌变的一种重要表现。本文提出了一种基于Multi-Agent(多智能体)的多分类器融合乳腺肿块分类方法。首先将单分类器的结果作为初状态输入到各Agent(智能体),接着通过引入决策共现矩阵,利用分类器之间的决策相关信息,在Agent之间进行信息交流,指导各个Agent向不同类别溯源,从而通过Agent之间的信息交互改变溯源概率,最终达到群体决策,得到决策类别。良性恶性肿块在形状和边缘上的差异较大,本文主要使用肿块的边缘特征和形状特征,并提出了两个新的边缘特征。实验采用了美国南佛罗里达大学的DDSM数据库作为实验数据,从中随机挑选了64个恶性肿块和64个良性肿块。实验结果表明,Multi-Agent融合算法的分类精度达94.87%优于传统的融合算法和经典的单分类器算法,其稳定性能也较融合算法及大多数单分类器的效果要好(略低于BP算法)。同时,实验结果也表明所提出的特征在表征肿块的良性恶性时起到了较好的作用。     

融合全局和局部特征的人脸识别算法

针对人脸识别算法缺乏对光照变化的自动调节能力的弱点,提出了一种综合利用全局和局部特征进行人脸识别的新方法。对整幅人脸图像进行主成分分析;针对人脸局部特征,提出一种根据各局部子块包含的信息量即利用图像熵值进行自动加权的算法;基于贝叶斯原理对全局和局部特征进行数据融合,给出最终结果。实验结果表明,该数据融合算法综合全局和局部特征提取方式的优势,有效提高了人脸识别率。     

一种基于改进的权值调整技术数据源分类算法研究*

针对传统的搜索引擎无法正确搜索到Deep Web中隐藏的海量信息,对Web数据库的分类是通向Web数据库分类集成和检索的关键步骤。提出了一种基于权值调整技术的Deep Web数据库分类方法,首先从网页表单中提取特征;然后对这些特征使用一种新的权重计算方法进行估值;最后利用朴素贝叶斯分类器对Web数据库进行分类。实验表明,这种分类方法经过少量样本训练后,就能达到很好的分类效果,并且随着训练样本的增加,该分类器的性能保持稳定,准确率、召回率都在很小的范围内波动。