纠错输出码综述

将纠错输出码运用到监督分类领域中可以有效的提高分类器的泛化能力,但目前还没有通用的确定性编码方法,该文通过对纠错输出编码框架的理解,介绍几种编码,能使读者掌握纠错输出编码的依据和整体。     

用纠错编码改进的M-ary支持向量机多类分类算法

针对M-ary支持向量机(SVM)多类分类算法结构简单,但泛化能力较弱的特点,提出了与纠错编码理论相结合的改进的M-ary SVM算法。首先,将原始类别信息编码作为信息码;然后结合纠错编码理论及期望的纠错能力,产生一定程度上性能最佳的编码,作为分类器训练的依据;最后,对于识别阶段输出编码中的错误分类利用检错纠错原理进行校正。实验结果表明,改进的算法通过引入尽可能少的冗余子分类器增强了标准M-ary SVM多类分类算法的性能。     

基于半监督技术的多分类器融合策略研究

提出一种新颖的多分类器构造方法,它以最大纠错能力作为分类器选择标准。实现时,采用半监督协同训练技术,充分利用单分类器的互补性,同时最大化仲裁器的仲裁能力,以提高多分类器系统的分类精度。在毒性数据集上的实验结果表明了方法的可行性和有效性。     

基于AUC方法评估多类别贝叶斯分类器的性能

分类器评估一般采用准确性评估.理论证明,基于AUC方法评估分类器优于准确性评估方法,但该方法局限于二类分类问题.提出一种将二类分类问题推广到多类分类问题的新方法,用纠错输出码转换得到转换矩阵,通过转换矩阵把多类分类问题转换成二类分类问题,计算二类分类的平均值来评估分类器的性能.新方法在MBNC实验平台下编程实现,并评估贝叶斯分类器的性能,实验结果表明,这种方法是有效的.     

基于特征空间变换的纠错输出编码

针对基于纠错输出编码多类分类中如何保证基分类器差异性的问题, 提出一种基于特征空间变换的编码方法. 该方法引入特征空间, 将编码矩阵扩展成三维矩阵; 然后基于二类划分, 利用特征变换得到不同的特征子空间, 从而训练得到差异性大的基分类器. 基于公共数据集的实验结果表明: 该方法能够比原始的编码矩阵获得更优的分类性能, 同时增加了基分类器的差异性; 该方法适用于任何编码矩阵, 为大数据的分类提供了新的思路.

     

基于最小二乘和纠错输出编码的多类分类

多类分类是目标识别中必须面对的一个关键问题,现有分类器大都为二分器,无法满足对多类目标进行分类,为此,提出利用纠错输出编码方法对多类问题进行分解,即把多类问题转化成二类问题;同时讨论一种基于最小二乘法对二分器结果进行融合的策略。实验分别对UCI数据集和三种一维距离像数据集进行测试,结果表明与经典的多分类器相比,提出的多类分类策略有较高的分类正确率。     

基于纠错编码的CSNN及其在遥感图像分类中的应用

单输出组合神经网络(CSNN)克服了BP神经网络固有的缺陷，具有网络结构确定、分类行为易于解释、并行性好等优点，但分类精度比经过结构选择的BPNN略差．采用纠错编码可以提高CSNN的分类精度，首先根据类别数与纠错能力确定类别码组，每个码字对应一种类别，每个SNN子网对这些码字中的同一位进行训练，从而确定网络结构与每个子网所学习的二值函数；对未知类别的样本进行分类时，各SNN的结果组成一个输出码，计算该输出码与各类别码的汉明距离，选择与其距离最近的类别码所对应的类别为该样本的类别；基于纠错编码的CSNN的分类行为易于转化为规则集形式，可理解性强．将该网络结构用于遥感图像分类，并与其他分类算法进行比较，结果表明采用纠错编码技术，CSNN不仅具备原有的各项优点，而且分类精度得到显著提高．     

先验知识引导下的基于遗传搜索的知识发现方法

针对知识库的不完备所导致的分类器泛化能力较差的问题,提出一种在先验知识引导下基于遗传算法的知识发现方法。该方法通过引入问题近似先验领域知识,进行种群初始化和变异函数构造,利用先验知识引导下的遗传算法对问题的解空间进行搜索,最终获取新知识。利用该方法可以获得同时覆盖先验领域知识和训练样例的一般知识,进而提高分类器的分类性能和泛化能力。实验结果表明,与经典遗传算法相比,不仅该算法的泛化能力更强,而且所获得特征规模较小。     

基于直觉模糊集的集成学习算法

为提高传统机器学习算法的分类精度和泛化能力,提出一种基于直觉模糊集的集成学习算法.根据传统分类器分类精度构建直觉模糊偏好关系矩阵,确定分类器权重,结合多属性群决策方法确定样本分类结果.在UCI中的7个数据集上进行测试,与目前流行的传统分类算法以及集成学习分类算法SVM,LR,NB,Boosting,Bagging相比,提出的算法分类平均精度分别提升了1.91％,3.89％,7.80％,3.66％,4.72％.该算法提高了传统分类方法的分类精度和泛化能力.     

多层组合分类器研究

为了提高监督分类的精度，本文从组合分类器的结构出发，提出一种横向多层组合模型，并对这种模型的运行方式与组合特性进行分析。该模型每层含有一个分类器，每个分类器的输入和输出一起作为其后面一层的输入。我们将简单贝叶斯法与BP神经网络组合成两层分类器。实验结果表明，这种组合方式有效地提高了单个方法的分类精度。     

Error-resilient pattern classification using a combination of spreading and coding gains

An approach that aims to enhance error resilience in pattern classification problems is proposed. The new approach combines the spread spectrum technique, specifically its selectivity and sensitivity, with error-correcting output codes (ECOC) for pattern classification. This approach combines both the coding gain of ECOC and the spreading gain of the spread spectrum technique to improve error resilience. ECOC is a well-established technique for general purpose pattern classification, which reduces the multi-class learning problem to an ensemble of two-class problems and uses special codewords to improve the error resilience of pattern classification. The direct sequence code division multiple access (DS-CDMA) technique is a spread spectrum technique that provides high user selectivity and high signal detection sensitivity, resulting in a reliable connection through a noisy radio communication channel shared by multiple users. Using DS-CDMA to spread the codeword, assigned to each pattern class by the ECOC technique, gives codes with coding properties that enable better correction of classification errors than ECOC alone. Results of performance assessment experiments show that the use of DS-CDMA alongside ECOC boosts error-resilience significantly, by yielding better classification accuracy than ECOC by itself.     

Research on the unbiased probability estimation of error-correcting output coding

Supervised classification based on error-correcting output codes (ECOC) is an efficient method to solve the problem of multi-class classification, and how to get the accurate probability estimation via ECOC is also an attractive research direction. This paper proposed three kinds of ECOC to get unbiased probability estimates, and investigated the corresponding classification performance in depth at the same time. Two evaluating criterions for ECOC that has better classification performance were concluded, which are Bayes consistence and unbiasedness of probability estimation. Experimental results on artificial data sets and UCI data sets validate the correctness of our conclusion.     

Stochastic organization of output codes in multiclass learning problems

The best-known decomposition schemes of multiclass learning problems are one per class coding (OPC) and error-correcting output coding (ECOC). Both methods perform a prior decomposition, that is, before training of the classifier takes place. The impact of output codes on the inferred decision rules can be experienced only after learning. Therefore, we present a novel algorithm for the code design of multiclass learning problems. This algorithm applies a maximum-likelihood objective function in conjunction with the expectation-maximization (EM) algorithm. Minimizing the augmented objective function yields the optimal decomposition of the multiclass learning problem in two-class problems. Experimental results show the potential gain of the optimized output codes over OPC or ECOC methods.     

基于KNN模型的层次纠错输出编码算法

纠错输出编码是一种解决多类分类问题的有效方法,但其编码矩阵只对类进行编码且都采用事先构造出来的统一形式,适应性较差。为此,提出一种新颖的层次纠错输出编码算法。该算法在训练阶段先通过KNN模型算法在数据集上构建多个同类簇,选取各类中最具代表性的簇形成层次编码矩阵,然后再根据编码矩阵进行单分类器训练。在测试阶段,该算法通过模型融合进一步发挥KNN模型和纠错输出编码各自的优点。在UCI公共数据集上的实验结果表明,新方法的性能优于KNN模型算法和纠错输出编码算法。     

基于子类划分和粒子群优化的自适应编码多类分类方法

纠错输出编码(ECOC)可以有效地解决多类分类问题.基于数据的编码是主要的编码方法之一.对此,提出一种基于子类划分和粒子群优化(PSO)的自适应编码方法,利用混淆矩阵衡量各类别的相关性,基于规则的方法对类别进行自适应组合,根据组合方案构建类别的二类划分并最终形成编码矩阵,通过引入PSO算法寻找最优阈值,从而得到最优编码矩阵.实验结果表明,所提出的编码方法可以得到更好的分类性能.     

基于纠错输出编码的人脸表情识别

多分类问题一直是模式识别领域的一个热点,提出了一种基于纠错输出编码和支持向量机的多分类器算法。根据通信编码理论设计纠错输出编码矩阵;按照该编码矩阵设计若干个互不相关的子支持向量机,根据编码原理将它们融合为一个多分类器。为了验证本分类器的有效性,采用Gabor小波提取人脸表情特征,应用二元主成分（2DPCA）分析法对提取的特征进行降维处理,应用该分类器进行了人脸表情的识别。实验结果表明,提出的方法能有效提高人脸表情的识别率,并具有极好的鲁棒性。     

Entanglement-assisted operator codeword stabilized quantum codes

In this paper, we introduce a unified framework to construct entanglement-assisted quantum error-correcting codes (QECCs), including additive and nonadditive codes, based on the codeword stabilized (CWS) framework on subsystems. The CWS framework is a scheme to construct QECCs, including both additive and nonadditive codes, and gives a method to construct a QECC from a classical error-correcting code in standard form. Entangled pairs of qubits (ebits) can be used to improve capacity of quantum error correction. In addition, it gives a method to overcome the dual-containing constraint. Operator quantum error correction (OQEC) gives a general framework to construct QECCs. We construct OQEC codes with ebits based on the CWS framework. This new scheme, entanglement-assisted operator codeword stabilized (EAOCWS) quantum codes, is the most general framework we know of to construct both additive and nonadditive codes from classical error-correcting codes. We describe the formalism of our scheme, demonstrate the construction with examples, and give several EAOCWS codes     

基于证据理论的纠错输出编码解决多类分类问题

针对多类分类问题,利用纠错输出编码作为分解框架,把多类问题转化为多个二类问题加以解决;同时提出一种基于证据理论的解码策略,把每一个二分器的输出作为证据之一进行融合,并讨论在两种编码类型(二元和三元编码矩阵)下证据融合的不同策略.通过实验分别对UCI数据集和3种一维距离像数据集进行测试,并与几种经典的解码方法进行比较,验证了所提出的方法能有效提高纠错输出编码特别是三元编码矩阵的分类正确率.     

Hierarchical classification method based on selective learning of slacked hierarchy for activity recognition systems

Physical activity recognition using wearable sensors has gained significant interest from researchers working in the field of ambient intelligence and human behavior analysis. The problem of multi-class classification is an important issue in the applications which naturally has more than two classes. A well-known strategy to convert a multi-class classification problem into binary sub-problems is the error-correcting output coding (ECOC) method. Since existing methods use a single classifier with ECOC without considering the dependency among multiple classifiers, it often fails to generalize the performance and parameters in a real-life application, where different numbers of devices, sensors and sampling rates are used. To address this problem, we propose a unique hierarchical classification model based on the combination of two base binary classifiers using selective learning of slacked hierarchy and integrating the training of binary classifiers into a unified objective function. Our method maps the multi-class classification problem to multi-level classification. A multi-tier voting scheme has been introduced to provide a final classification label at each level of the solicited model. The proposed method is evaluated on two publicly available datasets and compared with independent base classifiers. Furthermore, it has also been tested on real-life sensor readings for 3 different subjects to recognize four activities i.e. Walking, Standing, Jogging and Sitting. The presented method uses same hierarchical levels and parameters to achieve better performance on all three datasets having different number of devices, sensors and sampling rates. The average accuracies on publicly available dataset and real-life sensor readings were recorded to be 95% and 85%, respectively. The experimental results validate the effectiveness and generality of the proposed method in terms of performance and parameters.