一种改进的加权融合算法

该文以多个支持向量机分类器的输出向量为基础,运用决策模板来估计各个分类器的精度,然后使用常见的融合规则实现融合算法,并将其运用到蛋白质结构类分类当中。实验表明:该算法可有效提高分类精度,因此具有一定的应用价值。     

加权决策模板业务感知算法

针对决策模板法在业务感知准确率上的局限性问题,提出了加权决策模板法。该方法首先利用有监督的神经网络模糊聚类分类器作为基本分类器,再通过混淆矩阵衡量分类器对样本不同类别的置信度,经过两级的性能权衡,赋予该算法更高的可信度。在训练阶段根据错误分类的样本构造一个附加的加权决策模板,若在测试阶段有样本离该模板的距离最近时,可以认为该样本被错误分类的可能性很大,从而保证该算法具有高识别准确率。实验结果表明,与决策模板法对比,加权决策模板法在业务感知上具有更高的准确性。     

二叉树型多分类器融合的轴承故障诊断方法

多分类器融合能有效集成多种分类算法的优势,实现优势互补,提高智能诊断模型的稳健性和诊断精度。但在利用多数投票法构建多分类器融合决策系统时,要求成员分类器数目多于要识别的设备状态数,否则会出现无法融合的情况。针对此问题,提出了一种基于二叉树的多分类器融合算法,利用二叉树将多类分类问题转化为多个二值分类问题,从而各个节点上的成员分类器个数只要大于2即可,有效避免了成员分类器数目不足的问题。实验结果表明,相比单一分类器的诊断方法,该方法能有效地实现滚动轴承故障智能诊断,并具有对各神经网络初始值不敏感、识别率高且稳定等优势。     

基于类FP-tree的多层关联分类器

针对传统多层关联分类挖掘产生大量冗余规则而影响分类效率的问题,提出了一种基于类FP-tree的多层关联分类器MACCF(Multi-level Associative Classifier based on Class FP-tree)。该分类器依据事务的类标号划分训练集,采用闭频繁模式(CLOSET+)产生完全候选项目集,通过设计适当的类内规则剪枝策略和类间规则剪枝策略,减少了大量冗余的分类规则,提高了分类的准确率;采用交又关联规则方法,解决了交叉层数据的分类问题,实验结果 表明了算法的高效性。     

权值自适应调整的多分类器融合算法

针对度量层输出的多分类器融合,该文提出一种基于Multi-agent思想的融合算法。该算法给出样本集在多分类器下的偏好判断矩阵概念,可以根据各个样本的具体情况自适应地为各分类器赋予权值。实验证明,该算法可得到比其他方法更低的分类错误率。     

小样本下多稀疏表示分类器的决策融合方法

针对稀疏表示分类器的分类性能评估受样本数量影响较大,特别是在小样本情况下其分类精度低导致的强烈证据冲突问题,提出一种基于稀疏表示分类倾向性的决策融合方法.该方法采用稀疏分解重构残差的相对大小对样本在各个类别间的分类倾向性进行量化表征;通过求解残差异同概率,对稀疏分类器的混淆矩阵进行修正,提高了稀疏表示分类器分类性能评估的准确性;利用修正后的混淆矩阵对各个证据源进行加权融合,解决了小样本情况下的辨识精度低导致的高度证据冲突问题.在轴承故障融合诊断实验中,采用提出的方法对不同传感器信号的不同特征向量获得的稀疏表示分类器进行决策融合,达到了轴承故障状态准确辨识的目的,有效验证了所提出方法在小样本情况下进行目标分类识别的有效性与优势性.     

多分类器的一种动态联合方法

多分类器联合是解决复杂模式识别问题的有效办法。对于多分类器联合,一个关键的问题是如何对每个分类器的分类性能作出可靠性估计。以往提出的方法是利用各个分类器在训练阶段得到的知识来判断决策的可靠性,这些方法都需要大量的存储空间,并且没有考虑到分类器在分类过程中,由于输入样本的质量变化从而分类性能也会改变。文章提出了一种分类器的动态联合方法,该方法直接利用分类器的输出信息来估计分类器的可靠性。实验结果表明,比较传统的联合方法,该方法是一种有效的联合方法。     

基于多支持向量机的DT算法研究及应用

该文提出一种利用多个支持向量机分类器的输出向量来实现对分类器进行融合的决策模板算法,且采用交叉验证的方法得到模板,并将其运用到蛋白质结构类分类当中。实验表明:该文算法可有效提高分类精度,因此具有一定的应用价值。     

基于KNN+层次SVM的文本自动分类技术

针对大规模文本的自动层次分类问题,K近邻(KNN)算法分类效率较高,但是对于处于类别边界的样本分类准确度不是很高。而支持向量机(SVM)分类算法准确度比较高,但以前的多类SVM算法很多基于多个独立二值分类器组成,训练过程比较缓慢并且不适合层次类别结构等。提出一种融合KNN与层次SVM的自动分类方法。首先对KNN算法进行改进以迅速得到K个最近邻的类别标签,以此对文档的候选类别进行有效筛选。然后使用一个统一学习的多类稀疏层次SVM分类器对其进行自上而下的类别划分,从而实现对文档的高效准确的分类过程。实验结果表明,该方法在单层和多层的分类数据集上的分类准确度比单独使用其中任何一种要好,同时分类时间上也比较接近其中最快的单个分类器。     

基于类别信息的分类器集成方法Cagging

提出一种基于类别信息的分类器集成方法Cagging.基于类别信息重复选择样本生成基本分类器的训练集,增强了基本分类器之间的差异性;利用基本分类器对不同模式类的分类能力为每个基本分类器设置一组权重.使用权重对各分类器输出结果进行加权决策,较好地利用了各个基本分类器之间的差异性.在人脸图像库ORL上的实验验证了Cagging的有效性.此外,Cagging方法的基本分类器生成方式适合于通过增量学习生成集成分类器,扩展Cagging设计了基于增量学习的分类器集成方法Cagging-Ⅰ,实验验证了它的有效性.     

Combining classifiers using nearest decision prototypes

We present a new classifier fusion method to combine soft-level classifiers with a new approach, which can be considered as a generalized decision templates method. Previous combining methods based on decision templates employ a single prototype for each class, but this global point of view mostly fails to properly represent the decision space. This drawback extremely affects the classification rate in such cases: insufficient number of training samples, island-shaped decision space distribution, and classes with highly overlapped decision spaces. To better represent the decision space, we utilize a prototype selection method to obtain a set of local decision prototypes for each class. Afterward, to determine the class of a test pattern, its decision profile is computed and then compared to all decision prototypes. In other words, for each class, the larger the numbers of decision prototypes near to the decision profile of a given pattern, the higher the chance for that class. The efficiency of our proposed method is evaluated over some well-known classification datasets suggesting superiority of our method in comparison with other proposed techniques.     

A decision fusion method using an algorithm for fusion of correlated probabilities

This paper proposes a new decision fusion method accounting for conditional dependence (correlation) between land-cover classifications from multi-sensor data. The dependence structure between different classification results is calculated and used as weighting parameters for the subsequent fusion scheme. An algorithm for fusion of correlated probabilities (FCP) is adopted to fuse the prior probability, conditional probability, and obtained weighting parameters to generate a posterior probability for each class. A maximum posterior probability rule is then used to combine the posterior probabilities generated for each class to produce the final fusion result. The proposed FCP-based decision fusion method is assessed in land-cover classification over two study areas. The experimental results demonstrate that the proposed decision fusion method outperformed the existing decision fusion methods that do not take into account the correlation or dependence. The proposed decision fusion method can also be applied to other applications with different sensor data.     

基于粒度变换的多范畴复杂信息分类方法

针对大数据处理中多范畴复杂信息处理能力弱的问题,提出一种基于粒度变换的多范畴复杂信息对象的分类方法。首先在分类中引入映射表和误分类率阈值,然后构建等价类,设定对应不同范畴的属性,通过多种无损粒度变换和有损粒度变换的计算分析,获得多范畴复杂信息对象约泛化算子,并依此计算多范畴复杂信息分类对象的误分类率,并依误分类率对分类对象进行标引,实现有效分类。此方法在一定程度上解决多范畴复杂信息对象的分类问题,通过与另一套多范畴分类测试系统MCTS的对比,验证建立在此方法基础之上的多范畴复杂信息分类系统在误分类率上有较明显的优势。     

Code generation using an orthogonal model

This paper describes a new systematic approach to code generation. The approach is based on an orthogonal model, in which implementation of language-level operators (‘operators’) and addressing operators (‘operands’) is achieved by two independent subtasks. Each of these phases is specified using a set of decision trees that encode the set of possible implementation templates for each language feature and the set of constraints under which each can be applied. Code selection in each phase is achieved by interpreting these trees using a single comprehensive selection algorithm. The method easily extends to machine independence across a large class of target computers by abstracting the implementation templates into machine-independent implementation strategies. The selection algorithm is then modified to select between implementation strategies based on a machine capability ‘menu’ that describes each target machine in terms of the subset of implementation strategies for which it has corresponding instruction sequences. The method has been used to implement a prototype machine-independent code generator for the Concurrent Euclid programming language whose generated code density is consistently within four per cent of production machine-dependent code generators across its entire target class of five modern computers.     

基于Gabor小波变换和支持向量机的人脸识别

将Gabor小波变换和支持向量机分类方法结合起来进行人脸识别。通过由Gabor小波变换系数表示的若干个人脸模板和人脸图像之间的匹配来确定特征点的近似位置；在所有的特征点位置计算Gabor小波变换系数并将其串联成表示人脸图像的向量；采用一种层次分解的支持向量机二叉决策树进行分类识别。实验结果表明了该方法的可行性。     

面向混合数据的代价敏感三支决策边界域分类方法

针对现有三支决策模型的研究对象多为单一性数据的决策系统,对于混合数据边界域样本处理的研究相对较少,本文面向混合数据提出了基于核属性的代价敏感三支决策边界域分类方法。该方法基于正域约简计算混合邻域决策系统的核属性集,在此基础上计算混合邻域类,并利用三支决策规则分别将对象划分到各决策类的正域、边界域和负域;提出了一种基于代价敏感学习的三支决策边界域分类方法,并构造了误分类代价的计算方法,以此划分边界域中的对象。通过对UCI上的10个数据集进行实验对比与分析,进一步验证了本文方法,为处理边界域样本提供了一种可行有效的方法。     

Multiple subclass pattern recognition: A maximin correlationapproach

This paper addresses a correlation based nearest neighbor pattern recognition problem where each class is given as a collection of subclass templates. The recognition is performed in two stages. In the first stage the class is determined. Templates for this stage are created using the subclass templates. Assignment into subclasses occurs in the second stage. This two stage approach may be used to accelerate template matching. In particular, the second stage may be omitted when only the class needs to be determined. The authors present a method for optimal aggregation of subclass templates into class templates. For each class, the new template is optimal in that it maximizes the worst case (i.e. minimum) correlation with its subclass templates. An algorithm which solves this maximin optimization problem is presented and its correctness is proved. In addition, test results are provided, indicating that the algorithm's execution time is polynomial in the number of subclass templates. The authors show tight bounds on the maximin correlation. The bounds are functions only of the number of original subclass templates and the minimum element in their correlation matrix. The algorithm is demonstrated on a multifont optical character recognition problem     

A subspace decision cluster classifier for text classification

In this paper, a new classification method (SDCC) for high dimensional text data with multiple classes is proposed. In this method, a subspace decision cluster classification (SDCC) model consists of a set of disjoint subspace decision clusters, each labeled with a dominant class to determine the class of new objects falling in the cluster. A cluster tree is first generated from a training data set by recursively calling a subspace clustering algorithm Entropy Weighting k-Means algorithm. Then, the SDCC model is extracted from the subspace decision cluster tree. Various tests including Anderson–Darling test are used to determine the stopping condition of the tree growing. A series of experiments on real text data sets have been conducted. Their results show that the new classification method (SDCC) outperforms the existing methods like decision tree and SVM. SDCC is particularly suitable for large, high dimensional sparse text data with many classes.     

An automated approach to the design of decision tree classifiers

The classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data causes a significant computational problem. Decision tree classification is a popular approach to the problem. This type of classifier is characterized by the property that samples are subjected to a sequence of decision rules before they are assigned to a unique class. If a decision tree classifier is well designed, the result in many cases is a classification scheme which is accurate, flexible, and computationally efficient. This correspondence provides an automated technique for effective decision tree design which relies only on a priori statistics. This procedure utilizes canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classification is also provided. An example is given in which class statistics obtained from an actual Landsat scene are used as input to the program. The resulting decision tree design has an associated probability of correct classification of 0.75 compared to the theoretically optimum 0.79 probability of correct classification associated with a full dimensional Bayes classifier. Recommendations for future research are included.     

Region-based image retrieval with high-level semantics using decision tree learning

Semantic-based image retrieval has attracted great interest in recent years. This paper proposes a region-based image retrieval system with high-level semantic learning. The key features of the system are: (1) it supports both query by keyword and query by region of interest. The system segments an image into different regions and extracts low-level features of each region. From these features, high-level concepts are obtained using a proposed decision tree-based learning algorithm named DT-ST. During retrieval, a set of images whose semantic concept matches the query is returned. Experiments on a standard real-world image database confirm that the proposed system significantly improves the retrieval performance, compared with a conventional content-based image retrieval system. (2) The proposed decision tree induction method DT-ST for image semantic learning is different from other decision tree induction algorithms in that it makes use of the semantic templates to discretize continuous-valued region features and avoids the difficult image feature discretization problem. Furthermore, it introduces a hybrid tree simplification method to handle the noise and tree fragmentation problems, thereby improving the classification performance of the tree. Experimental results indicate that DT-ST outperforms two well-established decision tree induction algorithms ID3 and C4.5 in image semantic learning.