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。     

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.     

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.     

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).     

On influences of global and local cues on the rate of synchronization of oscillator networks

Synchronization of connected oscillator networks under global and local cues is ubiquitous in both science and engineering. Over the last few decades, enormous attention has been paid to study synchronization conditions of connected oscillators in chemistry, physics, mechanics, and particularly in biology. However, the influences of global and local cues on the rate of synchronization have not been fully studied. It is widespread that synchronization is achieved in the simultaneous presence of both global and local cues, such as intercellular coupling signals and external entrainment signals in terms of biological oscillators, and inter-neighbor coupling signals between follower nodes and central guiding signals in terms of groups of mobile autonomous agents. We prove in this paper that the strength of the global cue is the only determinant of the rate of synchronization. More specifically, we prove that a stronger global cue means a faster rate of synchronization whereas a stronger local cue does not necessarily make the synchronization rate faster. Our results not only apply to the noise-free case, but also apply to the case that the oscillator natural frequencies are subject to white noise. The analysis does not require the interplay to be symmetric or balanced. Simulation results are given to illustrate the proposed results.     

基于组合特征和级联分类器的防震锤检测算法

提出一种高压输电线路上的防震锤检测识别算法,算法基于分块的Haar特征、基于区域的LBP特征以及HOG特征一起作为组合特征来检测防震锤。其主要分为5个步骤:预处理待检测图像;改进归一化互相关匹配算法并进行模板匹配,得到防震锤疑似区域样本集;提取防震锤疑似区域的组合特征;对防震锤疑似区域使用级联分类器进行多级分类;统计分类结果。实验结果表明,该算法具有较高的精确率、召回率和准确率。     

基于全局和局部路径规划的人群运动研究与仿真

虚拟现实技术是多媒体技术广泛应用后兴起的计算机高新技术。大规模虚拟人群仿真得到了国内外很多学者的关注。大规模的虚拟人群仿真又分为人群绘制和路径规划两个研究方向。本文在复杂的环境中利用全局和局部路径二级规划算法,为大规模的虚拟人群实时规划出一条无碰撞的路径。     

基于贝叶斯评判子的字体判断

支票中的待识别文字既可能是印刷体,也可能是手写体．由于印刷体与手写体文字的预处理方法和识别算法不同,因此准确判断文字的字体(手写体或印刷体)是获得高精度识别结果的关键技术之一．该文根据贝叶斯决策理论的最小错误率判决规则,提出了基于评判子的字体判断方法．利用贝叶斯评判子,该文还提出了一种可分性判据：评判子散度;同时还给出了一种估算评判子函数的方法．在无拒识情况下,对12158张实际银行支票的测试中,该方法的正确率为99．4％．     

基于BSMOTE和逆转欠抽样的不均衡数据分类算法

针对传统分类器在数据不均衡的情况下分类效果不理想的缺陷,为提高分类器在不均衡数据集下的分类性能,特别是少数类样本的分类能力,提出了一种基于BSMOTE 和逆转欠抽样的不均衡数据分类算法。该算法使用BSMOTE进行过抽样,人工增加少数类样本的数量,然后通过优先去除样本中的冗余和噪声样本,使用逆转欠抽样方法逆转少数类样本和多数类样本的比例。通过多次进行上述抽样形成多个训练集合,使用Bagging方法集成在多个训练集合上获得的分类器来提高有效信息的利用率。实验表明,该算法较几种现有算法不仅能够提高少数类样本的分类性能,而且能够有效提高整体分类准确度。     

Listed companies’ financial distress prediction based on weighted majority voting combination of multiple classifiers

How to effectively predict financial distress is an important problem in corporate financial management. Though much attention has been paid to financial distress prediction methods based on single classifier, its limitation of uncertainty and benefit of multiple classifier combination for financial distress prediction has also been neglected. This paper puts forward a financial distress prediction method based on weighted majority voting combination of multiple classifiers. The framework of multiple classifier combination system, model of weighted majority voting combination, basic classifiers’ voting weight model and basic classifiers’ selection principles are discussed in detail. Empirical experiment with Chinese listed companies’ real world data indicates that this method can greatly improve the average prediction accuracy and stability, and it is more suitable for financial distress prediction than single classifiers.     

基于局部均值与类均值的近邻分类

k-近邻分类是一种流行且成功的非参数分类方法.但其分类性能由于离群点的存在而受到损害.为克服离群点对分类性能的不利影响,提出了一个k-近邻分类的变形和一个基于局部均值向量与类均值向量的近邻分类方法.该方法利用了未分类样本在每个训练类中k个近邻的局部均值的信息和整体均值的知识,不仅能够克服离群点对分类性能的影响,而且取得了比传统的k-近邻分类一致好的分类性能.     

一种局部与全局特征结合的图像检索算法

提出了一种基于稳定局部区域检测和全局图像特征描述的图像检索新算法。该算法继承了局部区域特征和GIST特征的优点,能够更精确地描述图像的内容,并且可以应对不同的视角变换．在一定程度上提高了图像检索的精度。     

Fingerprint matching based on global alignment of multiple reference minutiae

Fingerprint matching is an important problem in fingerprint identification. A set of minutiae is usually used to represent a fingerprint. Most existing fingerprint identification systems match two fingerprints using minutiae-based method. Typically, they choose a reference minutia from the template fingerprint and the query fingerprint, respectively. When matching the two sets of minutiae, the template and the query, firstly reference minutiae pair is aligned coordinately and directionally, and secondly the matching score of the rest minutiae is evaluated. This method guarantees satisfactory alignments of regions adjacent to the reference minutiae. However, the alignments of regions far away from the reference minutiae are usually not so satisfactory. In this paper, we propose a minutia matching method based on global alignment of multiple pairs of reference minutiae. These reference minutiae are commonly distributed in various fingerprint regions. When matching, these pairs of reference minutiae are to be globally aligned, and those region pairs far away from the original reference minutiae will be aligned more satisfactorily. Experiment shows that this method leads to improvement in system identification performance.     

Multi-scale local features based on anisotropic heat diffusion and global eigen-structure

Multi-scale local feature detection enables downstream registration and recognition tasks in med- ical image analysis. This paper articulates a novel robust method for multi-scale local feature extraction on volumetric data. The central idea is the elegant unification of local/global eigen-structures within the powerful framework of anisotropic heat diffusion. First, the local vector field is constructed by way of Hessian matrix and its eigenvectors/eigenvalues. Second, anisotropic heat kernels are computed using the vector field＇s global graph Laplacian. Robust local features are manifested as extrema across multiple time scales, serving as volumetric heat kernel signature. To tackle the computational challenge for massive volumetric data, we propose a multi- resolution strategy for hierarchical feature extraction based on our feature-preserving down-sampling approach. As a result, heat kernels and local feature identification can be approximated at a coarser level first, and then are pinpointed in a localized region at a finer resolution. Another novelty of this work lies at the initial heat design directly using local eigenvalue for anisotropic heat diffusion across the volumetric domain. We conduct experiments on various medical datasets, and draw comparisons with 3D SIFT method. The diffusion property of our local features, which can be interpreted as random walks in statistics, makes our method robust to noise, and gives rise to intrinsic multi-scale characteristics.     

Computing global visibility maps for regions on the boundaries of polyhedra using Minkowski sums

A global visibility map is a spherical image built to describe the complete set of global visible view directions for a surface. In this paper, we consider the computation of global visibility maps for regions on the boundary of a polyhedron. Both the self-occlusions introduced by a region and the global occlusions introduced by the rest of the surfaces on the boundary of the polyhedron are considered for computing a global visibility map. We show that the occluded view directions introduced between a pair of polyhedral surfaces can be computed from the spherical projection of the Minkowski sum of one surface and the reflection of the other. A suitable subset of the Minkowski sum, which shares the identical spherical projection with the complete Minkowski sum, is constructed to obtain the spherical images representing global occlusions. Our method has been successfully tested on many CAD models. It extends the previous methods for computing global visibility maps using convex decomposition, and it exhibits a better performance.     

大数据时代中文文本褒贬倾向性分类研究

在当前的大数据时代,互联网上的博客、论坛产生了海量的主观性评论信息,这些评论信息表达了人们的各种情感色彩和情感倾向性。如果仅仅用人工的方法来对网络上海量的评论信息进行分类和处理实在是太难了,那么,如何高效地挖掘出网络上大量的具有褒贬倾向性观点的信息就成为目前亟待解决的问题,中文文本褒贬倾向性分类技术研究正是解决这一问题的一个方法。文章介绍了常用的文本特征选择算法,分析了文档频率和互信息算法的不足,通过对两个算法的对比和研究,结合文本特征与文本类型的相关度和文本褒贬特征的出现概率,提出了改进的文本特征选择算法（MIDF）。实验结果表明,MIDF算法对文本褒贬倾向性分类是有效的。