Construction of an ensemble of logical correctors on the basis of elementary classifiers

A problem of constructing correct recognition algorithms on the basis of incorrect elementary classifiers is considered. A model of recognition procedures based on the construction of a family of logical correctors is proposed and analyzed. To this end, a genetic approach is applied that allows one, first, to reduce the computational cost and, second, to construct correctors with high recognition ability. This model is tested on real problems.     

Ensemble learning for independent component analysis

It is well known that the applicability of independent component analysis (ICA) to high-dimensional pattern recognition tasks such as face recognition often suffers from two problems. One is the small sample size problem. The other is the choice of basis functions (or independent components). Both problems make ICA classifier unstable and biased. In this paper, we propose an enhanced ICA algorithm by ensemble learning approach, named as random independent subspace (RIS), to deal with the two problems. Firstly, we use the random resampling technique to generate some low dimensional feature subspaces, and one classifier is constructed in each feature subspace. Then these classifiers are combined into an ensemble classifier using a final decision rule. Extensive experimentations performed on the FERET database suggest that the proposed method can improve the performance of ICA classifier.     

Logical correctors in recognition

Models of correct recognition algorithms are considered that are based on incorrect logical regularities called logical correctors. A new model of a logical corrector is constructed. The results of testing this model on real data are presented.     

Random Sampling for Subspace Face Recognition

Subspace face recognition often suffers from two problems: (1) the training sample set is small compared with the high dimensional feature vector; (2) the performance is sensitive to the subspace dimension. Instead of pursuing a single optimal subspace, we develop an ensemble learning framework based on random sampling on all three key components of a classification system: the feature space, training samples, and subspace parameters. Fisherface and Null Space LDA (N-LDA) are two conventional approaches to address the small sample size problem. But in many cases, these LDA classifiers are overfitted to the training set and discard some useful discriminative information. By analyzing different overfitting problems for the two kinds of LDA classifiers, we use random subspace and bagging to improve them respectively. By random sampling on feature vectors and training samples, multiple stabilized Fisherface and N-LDA classifiers are constructed and the two groups of complementary classifiers are integrated using a fusion rule, so nearly all the discriminative information is preserved. In addition, we further apply random sampling on parameter selection in order to overcome the difficulty of selecting optimal parameters in our algorithms. Then, we use the developed random sampling framework for the integration of multiple features. A robust random sampling face recognition system integrating shape, texture, and Gabor responses is finally constructed.     

Visual Learning by Evolutionary and Coevolutionary Feature Synthesis

In this paper, we present a novel method for learning complex concepts/hypotheses directly from raw training data. The task addressed here concerns data-driven synthesis of recognition procedures for real-world object recognition. The method uses linear genetic programming to encode potential solutions expressed in terms of elementary operations, and handles the complexity of the learning task by applying cooperative coevolution to decompose the problem automatically at the genotype level. The training coevolves feature extraction procedures, each being a sequence of elementary image processing and computer vision operations applied to input images. Extensive experimental results show that the approach attains competitive performance for three-dimensional object recognition in real synthetic aperture radar imagery.     

Support vector learning for fuzzy rule-based classification systems

To design a fuzzy rule-based classification system (fuzzy classifier) with good generalization ability in a high dimensional feature space has been an active research topic for a long time. As a powerful machine learning approach for pattern recognition problems, the support vector machine (SVM) is known to have good generalization ability. More importantly, an SVM can work very well on a high- (or even infinite) dimensional feature space. This paper investigates the connection between fuzzy classifiers and kernel machines, establishes a link between fuzzy rules and kernels, and proposes a learning algorithm for fuzzy classifiers. We first show that a fuzzy classifier implicitly defines a translation invariant kernel under the assumption that all membership functions associated with the same input variable are generated from location transformation of a reference function. Fuzzy inference on the IF-part of a fuzzy rule can be viewed as evaluating the kernel function. The kernel function is then proven to be a Mercer kernel if the reference functions meet a certain spectral requirement. The corresponding fuzzy classifier is named positive definite fuzzy classifier (PDFC). A PDFC can be built from the given training samples based on a support vector learning approach with the IF-part fuzzy rules given by the support vectors. Since the learning process minimizes an upper bound on the expected risk (expected prediction error) instead of the empirical risk (training error), the resulting PDFC usually has good generalization. Moreover, because of the sparsity properties of the SVMs, the number of fuzzy rules is irrelevant to the dimension of input space. In this sense, we avoid the "curse of dimensionality." Finally, PDFCs with different reference functions are constructed using the support vector learning approach. The performance of the PDFCs is illustrated by extensive experimental results. Comparisons with other methods are also provided.     

一种快速的自适应目标跟踪方法

由于光照变化、视角差异、相机抖动和部分遮挡等因素的影响,鲁棒的目标跟踪仍然是计算机视觉领域极具挑战性的研究课题.受协同训练和粒子滤波算法的启发,提出一种快速的自适应目标跟踪方法.该方法采用HOG(histogram of oriented gradients)和LBP(local binary pattern)描述目标特征并建立分类器,通过协同训练实现分类器的在线更新,有效解决了误差累积问题.为缩小目标搜索的状态空间,利用ICONDENSATION的运动模型和重要采样提高粒子采样的准确性和效率,并引入校正因子抑制虚假目标的干扰,从而提升了跟踪算法的鲁棒性和分类器更新的准确性.在两组标准测试集和两组自建测试集上的对比实验结果验证了所提出跟踪算法的有效性.与基于全局搜索的跟踪方法相比,该算法在不降低跟踪性能的前提下将处理速度提高25倍以上.     

Ensemble Online Classifier Based on the One-Class Base Classifiers for Mining Data Streams

The problem addressed in this study concerns mining data streams with concept drift. The goal of the article is to propose and validate a new approach to mining data streams with concept-drift using the ensemble classifier constructed from the one-class base classifiers. It is assumed that base classifiers of the proposed ensemble are induced from incoming chunks of the data stream. Each chunk consists of prototypes and information about whether the class prediction of these instances, carried-out at earlier steps, has been correct. Each data chunk can be updated by using the instance selection technique when new data arrive. When a new data chunk is formed, the ensemble model is also updated on the basis of weights assigned to each one-class classifier. In this article, two well-known instance-based learning algorithms—the CNN and the ENN—have been adopted to solve the one-class classification problems and, consequently, update the proposed classifier ensemble. The proposed approaches have been validated experimentally, and the computational experiment results are shown and discussed. The experiment results prove that the proposed approach using the ensemble classifier constructed from the one-class base classifiers with instance selection for chunk updating can outperform well-known approaches for data streams with concept drift.     

An intelligent web-page classifier with fair feature-subset selection

The explosion of on-line information has given rise to many manually constructed topic hierarchies (such as Yahoo!!). But with the current growth rate in the amount of information, manual classification in topic hierarchies results in an immense information bottleneck. Therefore, developing an automatic classifier is an urgent need. However, classifiers suffer from enormous dimensionality, since the dimensionality is determined by the number of distinct keywords in a document corpus. More seriously, most classifiers are either working slowly or they are constructed subjectively without any learning ability. In this paper, we address these problems with a fair feature-subset selection (FFSS) algorithm and an adaptive fuzzy learning network (AFLN) for classification. The FFSS algorithm is used to reduce the enormous dimensionality. It not only gives fair treatment to each category but also has ability to identify useful features, including both positive and negative features. On the other hand, the AFLN provides extremely fast learning ability to model the uncertain behavior for classification so as to correct the fuzzy matrix automatically. Experimental results show that both FFSS algorithm and the AFLN lead to a significant improvement in document classification, compared to alternative approaches.     

多类模式识别的动态多叉树算法研究与实现

研究模式识别方法，提出动态多叉树算法，用以解决实际环境中复杂的或大模式类别学习及系统动态扩展问题，该算法利用分治和局部最优原理缩小目标范围，结合整体学习方法提高识别率，模拟人脑的循序渐进学习方式，实现知识增殖和继承，可解决现有识别系统在学习新知识会破坏已有知识，需重新学习的问题，并具有较高的识别率，可有效地处理巨模式类识别的问题，该系统可以用于有脸、字符，指纹等对象的识别分类，系统的构造方法体现其通用性，性能分析表明其可行性，实验结果证明其有效性。     

基于组件词表的物体识别

提出一种基于组件词表的物体识别方法,通过AdaBoost从物体样本图像的组件中选取一些最具区分性的组件,构成组件词表。每幅图像都用词表中的组件来表征,在此基础上用稀疏神经网络来训练分类器。实验结果表明,该方法识别精度较高,对于遮挡和复杂背景有较强的鲁棒性。     

基于Stacking的恶意网页集成检测方法

针对目前主流恶意网页检测技术耗费资源多、检测周期长和分类效果低等问题，提出一种基于Stacking的恶意网页集成检测方法，将异质分类器集成的方法应用在恶意网页检测识别领域。通过对网页特征提取分析相关因素和分类集成学习来得到检测模型，其中初级分类器分别使用K近邻（KNN）算法、逻辑回归算法和决策树算法建立，而次级的元分类器由支持向量机（SVM）算法建立。与传统恶意网页检测手段相比，此方法在资源消耗少、速度快的情况下使识别准确率提高了0.7%，获得了98.12%的高准确率。实验结果表明，所提方法构造的检测模型可高效准确地对恶意网页进行识别。     

一种有效的不完整数据分类器

在模式识别、机器学习以及数据挖掘中,分类是一个基本而又重要的问题.虽有大量的分类器应运而生,但由于处理不完整数据的复杂性,它们大都是针对完整数据的.然而,由于各种原因,现实中的数据通常是不完整的.因此,对不完整数据分类器的研究具有重要意义.通过分析以往在分类过程中对不完整数据的处理方法,提出了一种不完整数据分类器:DBCI.在DBCI的训练过程中,将缺失值的频数按比例地分配到其它观测值的频数中.因此,不完整数据集所包含的信息可以得到充分利用.在12个标准的不完整数据集上的实验结果表明,与分类效果显著的不完整数据分类器RBC相比,DBCI具有更高的分类效率和更稳定的性能,并且它的分类准确率可以与RBC相媲美.     

基于卷积递归神经网络和核超限学习机的3D目标识别*

针对大规模RGB-D数据集中存在的深度线索质量和非线性模型分类问题,提出基于卷积递归神经网络和核超限学习机的3D目标识别方法.该方法引入深度图编码算法,修正原始深度图中存在的数值丢失和噪声问题,将点云图统一到标准角度,形成深度编码图,并结合原始深度图作为新的深度线索.利用卷积递归神经网络学习不同视觉线索的层次特征,融入双路空间金字塔池化方法,分别处理多线索特征.最后,构建基于核方法的超限学习机作为分类器,实现3D目标识别.实验表明,文中方法有效提高3D目标识别率和分类效率.     

Application of multi-layer perceptron neural networks to vision problems

This paper discusses the application of a class of feed-forward Artificial Neural Networks (ANNs) known as Multi-Layer Perceptrons(MLPs) to two vision problems: recognition and pose estimation of 3D objects from a single 2D perspective view; and handwritten digit recognition. In both cases, a multi-MLP classification scheme is developed that combines the decisions of several classifiers. These classifiers operate on the same feature set for the 3D recognition problem whereas different feature types are used for the handwritten digit recognition. The backpropagationlearning rule is used to train the MLPs. Application of the MLP architecture to other vision problems is also briefly discussed.     

BRACID: a comprehensive approach to learning rules from imbalanced data

In this paper we consider induction of rule-based classifiers from imbalanced data, where one class (a minority class) is under-represented in comparison to the remaining majority classes. The minority class is usually of primary interest. However, most rule-based classifiers are biased towards the majority classes and they have difficulties with correct recognition of the minority class. In this paper we discuss sources of these difficulties related to data characteristics or to an algorithm itself. Among the problems related to the data distribution we focus on the role of small disjuncts, overlapping of classes and presence of noisy examples. Then, we show that standard techniques for induction of rule-based classifiers, such as sequential covering, top-down induction of rules or classification strategies, were created with the assumption of balanced data distribution, and we explain why they are biased towards the majority classes. Some modifications of rule-based classifiers have been already introduced, but they usually concentrate on individual problems. Therefore, we propose a novel algorithm, BRACID, which more comprehensively addresses the issues associated with imbalanced data. Its main characteristics includes a hybrid representation of rules and single examples, bottom-up learning of rules and a local classification strategy using nearest rules. The usefulness of BRACID has been evaluated in experiments on several imbalanced datasets. The results show that BRACID significantly outperforms the well known rule-based classifiers C4.5rules, RIPPER, PART, CN2, MODLEM as well as other related classifiers as RISE or K-NN. Moreover, it is comparable or better than the studied approaches specialized for imbalanced data such as generalizations of rule algorithms or combinations of SMOTE + ENN preprocessing with PART. Finally, it improves the support of minority class rules, leading to better recognition of the minority class examples.     

A PARAMETER-BASED COMBINED CLASSIFIER FOR INVARIANT FACE RECOGNITION

A system for invariant face recognition is presented. A combined classifier uses the generalization capabilities of learning vector quantization (LVQ) neural networks to build a representative model of a face from a variety of training patterns with different poses, details, and facial expressions. The combined generalization error of the classifier is found to be lower than that of each individual classifier. The system is tested on an in-house built database and is capable of recognizing a face in about 1 second. The system performance compares favorably with the state-of-the-art systems. While the recognition rates of the individual classifiers ranged from 94% to 96%, a correct recognition rate of 100% is achieved by the combined classifier at 0% rejection.