基于主动学习提升朴素贝叶斯

提升(Boosting)是改善基分类器学习的有效手段。而研究表明,Boosting对于朴素贝叶斯的改善效果不明显。文章提出了一种新的提升算法——ActiveBoost,ActiveBoost结合主动学习挖掘未分配类别标注中样本的信息,并将不稳定性引入到朴素贝叶斯的构造过程。在UCI机器学习数据库的实验结果证明了该算法的有效性。     

一种结合半监督Boosting方法的迁移学习算法

迁移学习是数据挖掘中的一个研究方向,试图重用相关领域的数据样本,将相关领域的知识”迁移”到新领域中帮助训练.当前,基于实例的迁移学习算法容易产生过度拟合的问题,不能充分利用相关领域中的有用数据,为了避免这个问题,通过引入目标领域的无标记样本参与训练,利用半监督Boosting方法,提出一种新的迁移学习算法,能够对样本的...     

一种贝叶斯网络分类器集群式参数学习的降噪算法

文中首先分析降噪集成算法采用的样本置信度度量函数的性质,阐述此函数不适合处理多类问题的根源。进而设计更有针对性的置信度度量函数,并基于此函数提出一种增强型降噪参数集成算法。从而使鉴别式贝叶斯网络参数学习算法不但有效地抑止噪声影响,而且避免分类器的过度拟合,进一步拓展采用集群式学习算法的鉴别式贝叶斯网络分类器在多类问题上的应用。最后,实验结果及其统计假设检验分析充分验证此算法比目前的集群式贝叶斯网络参数学习方法得到的分类器在性能上有较显著提高。     

增强型朴素贝叶斯产

朴素贝叶斯是一种分类监督学习方法。在理论上,应用其前提为例子的属性值独立于例子的分类属性。这个前提在实际应用中过于严格,常常得不到满足,即使是这样,在违反该前提的情况下,朴素贝叶斯学习方法仍然取得了很大的成功。近来,一种改进的朴素贝叶斯方法,增强(Boost-ing),受到广泛的关注,AdaBoost方法是其主要方法。当AdaBoost方法被用于联合几个朴素贝叶斯分类器时,其在数学上等价于一个具有稀疏编码输入,单隐层节点,sigmoid激活函数的反馈型神经网络。     

Boosting Ensemble Learning for Hyperspectral Image Classification Using Tangent Collaborative Representation

Recently, Collaborative Representation Classification (CRC) has attracted much attention in hyperspectral image analysis. Due to uses the tangent plane to estimate the local manifold of the test sample. Tangent Collaborative Representation Classification (TCRC) achieve better performance. Furthermore, in order to improve the classification accuracy and reliability of hyperspectral remote sensing images, a novel Boosting-based Tangent Collaborative Representation ensemble method (Boost TCRC) for hyperspectral image classification is proposed. In this algorithm, Boost TCRC algorithm choose TCRC as base classifier and adjust the weight of the training samples adaptively by using the principle of Boosting. Increasing the weight of the misclassified samples so that the classifier concentrates on the training samples that are difficult to classify. Then assigns the weights according to the classification performance of the base classifier based on the residual domain fusion. Finally, the principle of minimum reconstruction error is adopted to classify the test sample. The performance of the proposed algorithm was comprehensively evaluated by hyperspectral remote sensing image data such as HyMap (Hyperspectral Mapper) and AVIRIS (Airbone Visible Infrared Imaging Spectrometer). The Boosting method can effectively improve the classification effect of the TCRC algorithm. For HyMap data, the overall classification accuracy and kappa coefficient of Boost TCRC algorithm are 93.73% and 0.920 8 respectively. Two precision values are higher than TCRC algorithm by 2.82% and 0.032 3, and are higher than the AdaBoost ELM algorithm by 1.81% and 0.022 5. For AVIRIS data, the overall classification accuracy and kappa coefficient of Boost TCRC algorithm are 84.11% and 0.8120 respectively. Two precision values are higher than TCRC algorithm by 3.97% and 0.049 3, and are higher than AdaBoost ELM algorithm by 12.02% and 0.143 6.     

基于Boosting的高光谱遥感切空间协同表示集成学习方法

近年来,协同表示分类(Collaborative Representation Classification,CRC)算法成为高光谱遥感影像分类的研究热点,尤其是切空间协同表示分类(Tangent Space Collaborative Representation,TCRC)利用切平面估计测试样本的局部流形,其分类精度得到了显著提高。为进一步提升高光谱遥感影像分类的准确性和可靠性,提出了基于Boosting的高光谱遥感影像切空间协同表示分类算法(Boosting-based Tangent Space Collaborative Representation Classification,Boost TCRC)。Boost TCRC算法采用TCRC算法作为基分类器,通过Boosting原理自适应地调整训练样本的权重,增大错分样本的权重从而使得分类器专注于较难分类的训练样本,然后在基于残差域融合时根据基分类器的分类表现赋予其权重,最终采用最小重构误差的原则对测试样本进行分类。实验采用HyMap(Hyperspectral Mapper)和AVIRIS(Airbone Visible Infrared Imaging Spectrometer)等高光谱遥感影像数据对所提出算法的性能进行了综合评价,结果表明：基于Boosting的集成方式可有效提升TCRC算法的分类效果。针对HyMap数据,Boost TCRC算法总体分类精度和Kappa系数分别为93.73%和0.920 8,两种精度指标分别高于TCRC算法2.82%和0.032 3,同时分别高于AdaBoost ELM算法1.81%和0.022 5。对于AVIRIS数据,Boost TCRC算法总体分类精度和kappa系数为84.11%和0.812 0,两种精度指标分别高于TCRC算法3.97%和0.049 3,同时分别高于AdaBoost ELM算法12.02%和0.143 6。     

一种半监督的多标签Boosting分类算法

针对标记数据不足的多标签分类问题,提出一种新的半监督Boosting算法,即基于函数梯度下降方法给出一种半监督Boosting多标签分类的框架,并将非标记数据的条件熵作为一个正则化项引入分类模型。实验结果表明,对于多标签分类问题,新的半监督Boosting算法的分类效果随着非标记数据数量的增加而显著提高,在各方面都优于传统的监督Boosting算法。     

一种用于分类的改进Boosting算法

提出了一种新的Boosting算法LAdaBoost。LAdaBoost算法利用局部错误率更新样本被选用于训练下一个分类器的概率,当对一个新的样本进行分类时,考虑了该样本与其邻域内的每个训练样本的近似度;另外,提出了有效邻域的概念。根据不同的组合方法,得到了两种LAdaBoost算法,即LAdaBoost-1和LAdaBoost-2。在UCI上部分实验数据集的实验结果表明,LAdaBoost算法比AdaBoost和Bagging算法更有效,且鲁棒性更好。     

基于Boosting算法的文本自动分类器设计

Boosting算法是目前流行的一种机器学习算法。采用一种改进的Boosting算法Adaboost.MHKR作为分类算法,设计了一个文本自动分类器,并给出了评估方法和结果。评价表明,该分类器有很好的分类精度。     

Multi-Class Segmentation with Relative Location Prior

Multi-class image segmentation has made significant advances in recent years through the combination of local and global features. One important type of global feature is that of inter-class spatial relationships. For example, identifying “tree” pixels indicates that pixels above and to the sides are more likely to be “sky” whereas pixels below are more likely to be “grass.” Incorporating such global information across the entire image and between all classes is a computational challenge as it is image-dependent, and hence, cannot be precomputed. In this work we propose a method for capturing global information from inter-class spatial relationships and encoding it as a local feature. We employ a two-stage classification process to label all image pixels. First, we generate predictions which are used to compute a local relative location feature from learned relative location maps. In the second stage, we combine this with appearance-based features to provide a final segmentation. We compare our results to recent published results on several multi-class image segmentation databases and show that the incorporation of relative location information allows us to significantly outperform the current state-of-the-art.     

稀疏Tikhonov正则化多分类支持向量机

Tikhonov正则化多分类支持向量机是一种将多分类问题简化为单个优化问题的新型支持向量机.由于Tikhonov正则化多分类支持向量机利用全部类别数据样本构建核函数矩阵,因此不适合大规模数据集的模式分类问题,鉴于该原因,一种稀疏Tikhonov正则化多分类支持量机被建立,其训练算法首先构建样本重要性评价标准,在标准下通过迭代学习获取约简集,最后利用约简集构建核函数矩阵并训练支持向量机.仿真实验结果表明稀疏Tikhonov正则化多分类支持向量机在训练速度和稀疏性方面具有很大的优越性.     

融合启发式和Boosting的子图匹配基数估计方法

由于在建模关联信息方面具备天然优势,图数据已在社交网络、知识表示等方面被广泛运用.但是相较于传统的关系型数据库系统,图数据管理中的以子图匹配为代表的一系列基础操作仍有进一步优化的空间.在一个完善的图数据库系统中,为实现多个子图匹配任务的优化调度,往往需要对每个任务的代价,尤其是匹配结果的基数进行准确预估.然而,现有的子...     

结合Boosting方法与SVM的多核学习跟踪算法

针对传统算法在外界环境及目标运动导致外形变化的影响下跟踪效果不稳定的问题,提出一种鲁棒的多核学习跟踪算法,将Boosting提升方法引入到多核学习框架中,用比传统多核学习算法更少的样本训练,构建出基于互补性特征集和核函数集的弱分类器池,从中将多个单核的弱分类器组合出一个多核的强分类器,从而在出现较强背景干扰、目标被遮挡的情况下仍能正确地对候选图块中的背景和目标进行分类。对不同视频序列的测试结果表明,与同样采用Boosting方法的OAB算法及近年跟踪精度高的LOT算法相比,该算法能够在复杂环境下更准确地跟踪到目标。     

Boosting recombined weak classifiers

Boosting is a set of methods for the construction of classifier ensembles. The differential feature of these methods is that they allow to obtain a strong classifier from the combination of weak classifiers. Therefore, it is possible to use boosting methods with very simple base classifiers. One of the most simple classifiers are decision stumps, decision trees with only one decision node.

This work proposes a variant of the most well-known boosting method, AdaBoost. It is based on considering, as the base classifiers for boosting, not only the last weak classifier, but a classifier formed by the last r selected weak classifiers (r is a parameter of the method). If the weak classifiers are decision stumps, the combination of r weak classifiers is a decision tree.

The ensembles obtained with the variant are formed by the same number of decision stumps than the original AdaBoost. Hence, the original version and the variant produce classifiers with very similar sizes and computational complexities (for training and classification). The experimental study shows that the variant is clearly beneficial.     

一种新的模糊补偿多类支持向量机

支持向量机是Vapnik等学者在统计学习理论的基础上提出的一种新的机器学习方法。针对支持向量机理论中的多类分类问题和对于噪音数据的敏感性,本文提出了一种模糊补偿多类支持向量机算法FC-SVM。该算法是在Weston等人提出的多类SVM分类器的直接构造方法中引入模糊补偿函数,针对每个输入数据对分类结果的两方面影响,将目标函数中的惩罚项不仅进行了模糊化,而且对于分类情况进行了加权补偿,并重构了优化问题及其约束条件,然后重构了Lagrange公式,给出了理论推导。在充分的数值实验基础上,将文中提出的方法应用于建设银行个人房贷的信用评估系统中,得到了较好的实验结果。     

Boosting for transfer learning from multiple data sources

Transfer learning aims at adapting a classifier trained on one domain with adequate labeled samples to a new domain where samples are from a different distribution and have no class labels. In this paper, we explore the transfer learning problems with multiple data sources and present a novel boosting algorithm, SharedBoost. This novel algorithm is capable of applying for very high dimensional data such as in text mining where the feature dimension is beyond several ten thousands. The experimental results illustrate that the SharedBoost algorithm significantly outperforms the traditional methods which transfer knowledge with supervised learning techniques. Besides, SharedBoost also provides much better classification accuracy and more stable performance than some other typical transfer learning methods such as the structural correspondence learning (SCL) and the structural learning in the multiple sources transfer learning problems.     

代价敏感的Boosting软件缺陷预测方法

软件缺陷预测可以有效提高软件的可靠性,修复系统存在的漏洞。Boosting重抽样是解决软件缺陷预测样本数量不足问题的常用方法,但常规Boosting方法在处理领域类不平衡问题时效果不佳。为此,提出一种代价敏感的Boosting软件缺陷预测方法CSBst。针对缺陷模块漏报和误报代价不同的问题,利用代价敏感的Boosting方法更新样本权重,增大产生第一类错误的样本权重,使之大于无缺陷类样本权重与第二类错误样本的权重,从而提高模块的预测率。采用阈值移动方法对多个决策树基分类器的分类结果进行集成,以解决过拟合问题。在此基础上,通过分析给出模型构建过程中权重和阈值的最优化设置。在NASA软件缺陷预测数据集上进行实验,结果表明,在小样本的情况下,与CSBKNN、CSCE方法相比,CSBst方法的BAL预测指标分别提升7%和3%,且时间复杂度降低一个数量级。     

多层梯度提升树在药品鉴别中的应用

近红外光谱分析技术高效应用于药品分析领域。针对高维非线性的小规模近红外数据,传统的药品鉴别算法存在特征学习能力不足的缺陷,基于神经网络的方法有局部最优及过拟合等问题,且两者易忽略样本的不均衡性。针对以上劣势,提出一种基于特征选择与代价敏感学习的多层梯度提升树(CS_FGBDT)药品分类方法。首先采用Savitsky-Golay平滑和一阶导数对原始数据进行预处理;其次利用随机森林对预处理光谱自适应提取特征,并由多层梯度提升树进行特征映射;然后结合代价敏感学习机制将样本不均衡性的负效应降到最小。实验结果表明,在胶囊和药片两种不平衡数据集上对算法进行对比评估,该模型具有更高的预测精度和稳定性,是一种有效的药品鉴别方法。     

基于双树复小波分解的Boosting集成学习土地覆被分类研究

近年来,集成学习（Ensemble Learning,EL）分类方法成为土地覆被分类的研究热点,尤其是Boosting集成分类方法具有分类精度高、泛化能力强,在土地覆被分类中得到了显著的应用。但是,Boosting集成分类方法对噪声很敏感,如果训练样本含有噪声时,Boosting算法可能会失效,这是该方法的局限性。为了解决Boosting集成方法在土地覆被分类中存在的问题,有效克服噪声的影响,减少分类结果中的“椒盐”现象和提高分类精度,提出了基于双树复小波分解的Boosting集成学习分类方法。该方法对影像的光谱波段进行一层双树复小波分解,降低图像的噪声,将分解后的各波段作为Boosting集成学习的输入,得到最终的分类结果。实验先后比较了GBDT、XGBoost、LightGBM 3种Boosting集成学习算法在SPOT 6和Sentinel-2A影像上的分类效果。结果表明：(1)在SPOT 6影像上,3种Boosting集成算法总体分类精度均高于90%;DTCWTLightGBM分类总体精度最高,达到94.73%,Kappa系数为0.93,比LightGBM总体精度提高了1.1%...