Ensemble classifier based source camera identification using fusion features

Multimedia Tools and Applications - Source camera identification, which means identifying the camera source of a given image, has become one of the most important branches of digital image...     

利用语音与文本特征融合改善语音情感识别

情感识别在人机交互中具有重要意义,为了提高情感识别准确率,将语音与文本特征融合。语音特征采用了声学特征和韵律特征,文本特征采用了基于情感词典的词袋特征（Bag-of-words,BoW）和N-gram模型。将语音与文本特征分别进行特征层融合与决策层融合,比较它们在IEMOCAP四类情感识别的效果。实验表明,语音与文本特征融合比单一特征在情感识别中表现更好;决策层融合比在特征层融合识别效果好。且基于卷积神经网络（Convolutional neural network,CNN）分类器,语音与文本特征在决策层融合中不加权平均召回率（Unweighted average recall,UAR)达到了68.98%,超过了此前在IEMOCAP数据集上的最好结果。     

Genetic algorithms in classifier fusion

An intense research around classifier fusion in recent years revealed that combining performance strongly depends on careful selection of classifiers to be combined. Classifier performance depends, in turn, on careful selection of features, which could be further restricted by the subspaces of the data domain. On the other hand, there is already a number of classifier fusion techniques available and the choice of the most suitable method depends back on the selections made within classifier, features and data spaces. In all these multidimensional selection tasks genetic algorithms (GA) appear to be one of the most suitable techniques providing reasonable balance between searching complexity and the performance of the solutions found. In this work, an attempt is made to revise the capability of genetic algorithms to be applied to selection across many dimensions of the classifier fusion process including data, features, classifiers and even classifier combiners. In the first of the discussed models the potential for combined classification improvement by GA-selected weights for the soft combining of classifier outputs has been investigated. The second of the proposed models describes a more general system where the specifically designed GA is applied to selection carried out simultaneously along many dimensions of the classifier fusion process. Both, the weighted soft combiners and the prototype of the three-dimensional fusion–classifier–feature selection model have been developed and tested using typical benchmark datasets and some comparative experimental results are also presented.     

Fuzzy classifier fusion: an application to reservoir facies identification

An application of classifier fusion technique is presented to improve the performance of automated reservoir facies identification system. The algorithm presented in this study uses three well-known classifiers, namely Bayesian, k-nearest neighbor (kNN), and support vector machine (SVM) to automatically identify four defined facies of Asmari Formation from log-derived amplitude versus offset (AVO) attributes. Fuzzy Sugeno integral (FSI) method is then employed to combine the outputs of three investigated classifiers and increase the consistency of reservoir facies identification process. The experimental results obtained from applying the presented algorithm on data related to three wells drilled in Asmari Formation provide evidence of the effectiveness of the proposed algorithm regarding true positive (TP), false positive (FP), and classification accuracy criteria.

     

Designing classifier fusion systems by genetic algorithms

We suggest two simple ways to use a genetic algorithm (GA) to design a multiple-classifier system. The first GA version selects disjoint feature subsets to be used by the individual classifiers, whereas the second version selects (possibly) overlapping feature subsets, and also the types of the individual classifiers. The two GAs have been tested with four real data sets: heart, Satimage, letters, and forensic glasses. We used three-classifier systems and basic types of individual classifiers (the linear and quadratic discriminant classifiers and the logistic classifier). The multiple-classifier systems designed with the two GAs were compared against classifiers using: all features; the best feature subset found by the sequential backward selection method; and the best feature subset found by a CA. The GA design can be made less prone to overtraining by including penalty terms in the fitness function accounting for the number of features used.     

Sample-specific late classifier fusion for speaker verification

Multimedia Tools and Applications - Due to the mismatch between training and test conditions, speaker verification in real environments, continues to be a challenging problem. An effective way of...     

An empirical risk functional to improve learning in a neuro-fuzzy classifier.

The paper proposes a new Empirical Risk Functional as cost function for training neuro-fuzzy classifiers. This cost function, called Approximate Differentiable Empirical Risk Functional (ADERF), provides a differentiable approximation of the misclassification rate so that the Empirical Risk Minimization Principle formulated in Vapnik's Statistical Learning Theory can be applied. Also, based on the proposed ADERF, a learning algorithm is formulated. Experimental results on a number of benchmark classification tasks are provided and comparison to alternative approaches given.     

Adaptive fusion and co-operative training for classifier ensembles

In this paper, architectures and methods of decision aggregation in classifier ensembles are investigated. Typically, ensembles are designed in such a way that each classifier is trained independently and the decision fusion is performed as a post-process module. In this study, however, we are interested in making the fusion a more adaptive process. We first propose a new architecture that utilizes the features of a problem to guide the decision fusion process. By using both the features and classifiers outputs, the recognition strengths and weaknesses of the different classifiers are identified. This information is used to improve overall generalization capability of the system. Furthermore, we propose a co-operative training algorithm that allows the final classification to determine whether further training should be carried out on the components of the architecture. The performance of the proposed architecture is assessed by testing it on several benchmark problems. The new architecture shows improvement over existing aggregation techniques. Moreover, the proposed co-operative training algorithm provides a means to limit the users’ intervention, and maintains a level of accuracy that is competitive to that of most other approaches.     

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 classifier fusion system for bearing fault diagnosis

In this paper, a new strategy based on the fusion of different Support Vector Machines (SVM) is proposed in order to reduce noise effect in bearing fault diagnosis systems. Each SVM classifier is designed to deal with a specific noise configuration and, when combined together – by means of the Iterative Boolean Combination (IBC) technique – they provide high robustness to different noise-to-signal ratio. In order to produce a high amount of vibration signals, considering different defect dimensions and noise levels, the BEAring Toolbox (BEAT) is employed in this work. The experiments indicate that the proposed strategy can significantly reduce the error rates, even in the presence of very noisy signals.     

On information fusion to improve segmentation of MRI sequences

The paper deals with the problem of segmentation of MRI sequences of vertebrae, in the form of images of their multiple slices, using the Dempster–Shafer theory. This leads to the study of 3-D deformations of the scoliosis. The motivation comes from the inadequacy of the existing techniques based on X-ray image analysis. Such analysis cannot deal with, on the one hand, the complex anatomical structures (“scoliotic rachis”), and the spongy tissue peri-rachidian, and, on the other hand, the choice of slices and the problem of the residue irradiation present in each examination. The main contributions of the paper are:

• •New architecture for the fusion of MRI data sets.
• •A novel method to exploit the information contained in MRI sequence.
• •Model for knowledge representation adapted to specificity of information available (Dempster–Shafer theory).
• •Choice of the discriminating parameters for the statistical expertise.
• •Construction of the belief functions.
• •Choice of the decision criterion.

Starting from segmentation by active contour (snake) [Deformable contour: modelling, extraction, detection and classification, Ph.D. Thesis, Wisconsin–Madison University, 1994; Proceedings of the 15th International Conference on Pattern Recognition, vol. 4, Barcelona, 2000, p. 17; Int. J. Comput. Vision 1 (3) (1987) 211], we upgrade it in an attempt to present the doctor with a degree of belief concerning their membership of the contour of the vertebra. We illustrate the proposed fusion architecture by application to actual MRI sequences of the vertebrae, and include perhaps the first example of 3-D reconstruction of the lumbar rachis starting from the results obtained during fusion.     

多类型分类器融合的文本分类方法研究

传统的文本分类方法大多数使用单一的分类器,而不同的分类器对分类任务的侧重点不同,就使得单一的分类方法有一定的局限性,同时每个特征提取方法对特征词的考虑角度不同。针对以上问题,提出了多类型分类器融合的文本分类方法。该模型使用了word2vec、主成分分析、潜在语义索引以及TFIDF特征提取方法作为多类型分类器融合的特征提取方法。并在多类型分类器加权投票方法中忽略了类别信息的问题,提出了类别加权的分类器权重计算方法。通过实验结果表明,多类型分类器融合方法在二元语料库、多元语料库以及特定语料库上都取得了很好的性能,类别加权的分类器权重计算方法比多类型分类器融合方法在分类性能方面提高了1.19%。     

An investigation of the effects of correlation and autocorrelation on classifier fusion and optimal classifier ensembles

The purpose of this research was to study various fusion strategies where the levels of correlation between features and auto-correlation within features could be controlled. The fusion strategies were chosen to reflect decision-level fusion (ISOC and ROC), feature level fusion, via a single Generalized Regression Neural Network (GRNN) employing all available features, and an intermediate level of fusion that employed the outputs of individual classifiers, in this case posterior probability estimates, before they are subjected to thresholds and mapped into decisions. This latter scheme involved fusing the posterior probability estimates by employing them as features in a probabilistic neural network. Correlation was injected into the data set both within a feature set (auto-correlation) and across feature sets, and sample size was varied for a two class problem. The fusion methods were then extended to three classifiers, and a method is demonstrated that selects the optimal classifier ensemble.     

A way to improve an architecture of neural network classifier for remote sensing applications

Recent results in neural network research have demonstrated their utility in a variety of application areas. Neural networks are able to achieve a very high performance, and classification accuracy in real world applications such as handwritten character recognition, remote sensing images, vision, robotic. Network performance greatly depends not only on the input/output data, but also on its architecture. Most of neural network applications have been developed using anad hoc approach resulting in poor efficiency and performance. In this paper, a development method of neural network applications is presented, and illustrated with a neural classifier of remote sensing images. It is shown how to create in an iterative way a neural classifier architecture, and how to refine a network organization using performance evaluation criteria.     

Limits on the majority vote accuracy in classifier fusion

We derive upper and lower limits on the majority vote accuracy with respect to individual accuracy p, the number of classifiers in the pool (L), and the pairwise dependence between classifiers, measured by Yule’s Q statistic. Independence between individual classifiers is typically viewed as an asset in classifier fusion. We show that the majority vote with dependent classifiers can potentially offer a dramatic improvement both over independent classifiers and over an individual classifier with accuracy p. A functional relationship between the limits and the pairwise dependence Q is derived. Two patterns of the joint distribution for classifier outputs (correct/incorrect) are identified to derive the limits: the pattern of success and the pattern of failure. The results support the intuition that negative pairwise dependence is beneficial although not straightforwardly related to the accuracy. The pattern of success showed that for the highest improvement over p, all pairs of classifiers in the pool should have the same negative dependence. ID="A1"Correspondance and offprint requests to: L. I. Kuncheva, School of Informatics, University of Wales, Bangor LL57 1UT, Gwynedd, UK. Email: l.i.kuncheva@bangor.ac.uk     

A multisensor fusion approach to improve LAI time series

High-quality and gap-free satellite time series are required for reliable terrestrial monitoring. Moderate resolution sensors provide continuous observations at global scale for monitoring spatial and temporal variations of land surface characteristics. However, the full potential of remote sensing systems is often hampered by poor quality or missing data caused by clouds, aerosols, snow cover, algorithms and instrumentation problems. A multisensor fusion approach is here proposed to improve the spatio-temporal continuity, consistency and accuracy of current satellite products. It is based on the use of neural networks, gap filling and temporal smoothing techniques. It is applicable to any optical sensor and satellite product. In this study, the potential of this technique was demonstrated for leaf area index (LAI) product based on MODIS and VEGETATION reflectance data. The FUSION product showed an overall good agreement with the original MODIS LAI product but exhibited a reduction of 90% of the missing LAI values with an improved monitoring of vegetation dynamics, temporal smoothness, and better agreement with ground measurements.     

基于成像传感器特征的相机分类器设计

在对现有成像设备源辨识算法分析研究的基础上,提出一种利用成像传感器特征进行相机源辨识的鲁棒性方法.基于模式分类的原理,首先分析数码相机成像的特点,提取传感器噪声信息的统计特征,设计一种鲁棒的分类器来确定相机的品牌/型号.所提取的图像特征包括图像去噪差值和小波域分析.结果表明:所设计的分类器可以有效地正确辨识相机品牌/型...     

属性约减与分类器融合实时入侵检测研究

针对入侵检测系统存在的对入侵事件高漏报率和误报率问题,提出利用粗糙集理论对数据集中的实值属性进行属性约简,然后把得到的特征向量送入分类器融合的Robust Online-SVM分类器,分类器对这些数字向量进行分类,处理结果送检测模块。检测模块按照报警关联分析策略,对报警序列进行基于规则关联分析。通过实验和比较发现,该融合算法可以实现在线训练,而且使用更少的支持向量,训练时间也大为缩短,在噪声数据存在的情况下检测正确率和虚警率比未改进前有一定程度的提升。