Enhancing multi-label classification by modeling dependencies among labels

In this paper, we propose a novel framework for multi-label classification, which directly models the dependencies among labels using a Bayesian network. Each node of the Bayesian network represents a label, and the links and conditional probabilities capture the probabilistic dependencies among multiple labels. We employ our Bayesian network structure learning method, which guarantees to find the global optimum structure, independent of the initial structure. After structure learning, maximum likelihood estimation is used to learn the conditional probabilities among nodes. Any current multi-label classifier can be employed to obtain the measurements of labels. Then, using the learned Bayesian network, the true labels are inferred by combining the relationship among labels with the labels? estimates obtained from a current multi-labeling method. We further extend the proposed multi-label classification method to deal with incomplete label assignments. Structural Expectation-Maximization algorithm is adopted for both structure and parameter learning. Experimental results on two benchmark multi-label databases show that our approach can effectively capture the co-occurrent and the mutual exclusive relation among labels. The relation modeled by our approach is more flexible than the pairwise or fixed subset labels captured by current multi-label learning methods. Thus, our approach improves the performance over current multi-label classifiers. Furthermore, our approach demonstrates its robustness to incomplete multi-label classification.     

The use of data-derived label hierarchies in multi-label classification

Instead of traditional (multi-class) learning approaches that assume label independency, multi-label learning approaches must deal with the existing label dependencies and relations. Many approaches try to model these dependencies in the process of learning and integrate them in the final predictive model, without making a clear difference between the learning process and the process of modeling the label dependencies. Also, the label relations incorporated in the learned model are not directly visible and can not be (re)used in conjunction with other learning approaches. In this paper, we investigate the use of label hierarchies in multi-label classification, constructed in a data-driven manner. We first consider flat label sets and construct label hierarchies from the label sets that appear in the annotations of the training data by using a hierarchical clustering approach. The obtained hierarchies are then used in conjunction with hierarchical multi-label classification (HMC) approaches (two local model approaches for HMC, based on SVMs and PCTs, and two global model approaches, based on PCTs for HMC and ensembles thereof). The experimental results reveal that the use of the data-derived label hierarchy can significantly improve the performance of single predictive models in multi-label classification as compared to the use of a flat label set, while this is not preserved for the ensemble models.     

多标签代价敏感分类集成学习算法

尽管多标签分类问题可以转换成一般多分类问题解决,但多标签代价敏感分类问题却很难转换成多类代价敏感分类问题.通过对多分类代价敏感学习算法扩展为多标签代价敏感学习算法时遇到的一些问题进行分析,提出了一种多标签代价敏感分类集成学习算法.算法的平均错分代价为误检标签代价和漏检标签代价之和,算法的流程类似于自适应提升（Adaptive boosting,AdaBoost）算法,其可以自动学习多个弱分类器来组合成强分类器,强分类器的平均错分代价将随着弱分类器增加而逐渐降低.详细分析了多标签代价敏感分类集成学习算法和多类代价敏感AdaBoost算法的区别,包括输出标签的依据和错分代价的含义.不同于通常的多类代价敏感分类问题,多标签代价敏感分类问题的错分代价要受到一定的限制,详细分析并给出了具体的限制条件.简化该算法得到了一种多标签AdaBoost算法和一种多类代价敏感AdaBoost算法.理论分析和实验结果均表明提出的多标签代价敏感分类集成学习算法是有效的,该算法能实现平均错分代价的最小化.特别地,对于不同类错分代价相差较大的多分类问题,该算法的效果明显好于已有的多类代价敏感AdaBoost算法.     

Missing multi-label learning with non-equilibrium based on two-level autoencoder

For a multi-label learning framework, each instance may belong to multiple labels simultaneously. The classification accuracy can be improved significantly by exploiting various correlations, such as label correlations, feature correlations, or the correlations between features and labels. There are few studies on how to combine the feature and label correlations, and they deal more with complete data sets. However, missing labels or other phenomena often occur because of the cost or technical limitations in the data acquisition process. A few label completion algorithms currently suitable for missing multi-label learning, ignore the noise interference of the feature space. At the same time, the threshold of the discriminant function often affects the classification results, especially those of the labels near the threshold. All these factors pose considerable difficulties in dealing with missing labels using label correlations. Therefore, we propose a missing multi-label learning algorithm with non-equilibrium based on a two-level autoencoder. First, label density is introduced to enlarge the classification margin of the label space. Then, a new supplementary label matrix is augmented from the missing label matrix with the non-equilibrium label completion method. Finally, considering feature space noise, a two-level kernel extreme learning machine autoencoder is constructed to implement the information feature and label correlation. The effectiveness of the proposed algorithm is verified by many experiments on both missing and complete label data sets. A statistical analysis of hypothesis validates our approach.

     

Multi-label classification by exploiting label correlations

Nowadays, multi-label classification methods are of increasing interest in the areas such as text categorization, image annotation and protein function classification. Due to the correlation among the labels, traditional single-label classification methods are not directly applicable to the multi-label classification problem. This paper presents two novel multi-label classification algorithms based on the variable precision neighborhood rough sets, called multi-label classification using rough sets (MLRS) and MLRS using local correlation (MLRS-LC). The proposed algorithms consider two important factors that affect the accuracy of prediction, namely the correlation among the labels and the uncertainty that exists within the mapping between the feature space and the label space. MLRS provides a global view at the label correlation while MLRS-LC deals with the label correlation at the local level. Given a new instance, MLRS determines its location and then computes the probabilities of labels according to its location. The MLRS-LC first finds out its topic and then the probabilities of new instance belonging to each class is calculated in related topic. A series of experiments reported for seven multi-label datasets show that MLRS and MLRS-LC achieve promising performance when compared with some well-known multi-label learning algorithms.     

基于结构性质保持和相关性学习的多标记分类算法

现有的多标记学习技术大多只考虑了相关性学习问题而忽略了数据因变换而引起的结构性质不一致问题,导致原始特征数据的结构性质因映射变换发生改变,从而影响了模型的分类性能。为了解决这一问题,提出了基于结构性质保持和相关性学习的多标记分类算法。首先,构造了线性映射函数以实现特征空间与标记空间的映射;然后借鉴图正则化思想,引入基于特征数据的结构性质保持策略以降低特征数据因线性变换引起的结构性质差异;最后,针对标记数据引入基于标记对的相关性学习策略进一步优化算法参数,以提高模型的分类性能。在不同规模的标准数据集上进行测试,结果表明所提算法与一些流行的多标记分类算法相比具有更优的分类性能,验证了所提算法的有效性。     

ML-KNN: A lazy learning approach to multi-label learning

Multi-label learning originated from the investigation of text categorization problem, where each document may belong to several predefined topics simultaneously. In multi-label learning, the training set is composed of instances each associated with a set of labels, and the task is to predict the label sets of unseen instances through analyzing training instances with known label sets. In this paper, a multi-label lazy learning approach named ML-KNN is presented, which is derived from the traditional K-nearest neighbor (KNN) algorithm. In detail, for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, maximum a posteriori (MAP) principle is utilized to determine the label set for the unseen instance. Experiments on three different real-world multi-label learning problems, i.e. Yeast gene functional analysis, natural scene classification and automatic web page categorization, show that ML-KNN achieves superior performance to some well-established multi-label learning algorithms.     

Compositional metric learning for multi-label classification

Multi-label classification aims to assign a set of proper labels for each instance, where distance metric learning can help improve the generalization ability of instance-based multi-label classification models. Existing multi-label metric learning techniques work by utilizing pairwise constraints to enforce that examples with similar label assignments should have close distance in the embedded feature space. In this paper, a novel distance metric learning approach for multi-label classification is proposed by modeling structural interactions between instance space and label space. On one hand, compositional distance metric is employed which adopts the representation of a weighted sum of rank-1 PSD matrices based on component bases. On the other hand, compositional weights are optimized by exploiting triplet similarity constraints derived from both instance and label spaces. Due to the compositional nature of employed distance metric, the resulting problem admits quadratic programming formulation with linear optimization complexity w.r.t. the number of training examples.We also derive the generalization bound for the proposed approach based on algorithmic robustness analysis of the compositional metric. Extensive experiments on sixteen benchmark data sets clearly validate the usefulness of compositional metric in yielding effective distance metric for multi-label classification.     

基于标记特征的多标记学习改进算法

基于标记特征的多标记分类算法通过对标记的正反样例集合进行聚类,计算样例与聚类中心间的距离构造样例针对标记的特征子集,并生成新的训练集,在新的训练集上利用传统的二分类器进行分类。算法在构造特征子集的过程中采用等权重方式,忽略了样例之间的相关性。提出了一种改进的多标记分类算法,通过加权方式使生成的特征子集更加准确,有助于提高样例的分类精度。实验表明改进的算法性能优于其他常用的多标记分类算法。     

基于密度核估计的贝叶斯网络结构学习算法

贝叶斯网络结构学习算法主要包括爬山法和K2算法等,但这些方法均要求面向大样本数据集。针对实际问题中样本集规模小的特点,通过引入概率密度核估计方法以实现对原始样本集的拓展,利用K2算法进行贝叶斯网络结构学习。通过优化选择核函数和窗宽,基于密度核估计方法实现了样本集的有效扩展;同时基于互信息度进行变量顺序的确认,进而建立了小规模样本集的贝叶斯结构学习算法。仿真结果验证了新学习算法的有效性和实用性。     

基于Tri-training的半监督多标记学习算法

传统的多标记学习是监督意义下的学习,它要求获得完整的类别标记.但是当数据规模较大且类别数目较多时,获得完整类别标记的训练样本集是非常困难的.因而,在半监督协同训练思想的框架下,提出了基于Tri-training的半监督多标记学习算法（SMLT）.在学习阶段,SMLT引入一个虚拟类标记,然后针对每一对类别标记,利用协同训练机制Tri-training算法训练得到对应的分类器;在预测阶段,给定一个新的样本,将其代入上述所得的分类器中,根据类别标记得票数的多少将多标记学习问题转化为标记排序问题,并将虚拟类标记的得票数作为阈值对标记排序结果进行划分.在UCI中4个常用的多标记数据集上的对比实验表明,SMLT算法在4个评价指标上的性能大多优于其他对比算法,验证了该算法的有效性.     

用于多标记学习的局部顺序分类器链算法

标记间的相关性在分类问题中具有重要作用,目前有研究将标记相关性引入多标记学习,通过分类器链的形式将标记结果引入属性空间,为学习其他标记提供有用信息。分类器链中标记的预测顺序具有随机性,分类结果存在着很大的不确定性与不稳定性,且容易造成错误信息的传播。为此充分考虑标记的局部分布特性,提出了一种局部顺序分类器链算法,解决分类器链中分类器顺序问题。实验表明,该算法性能优于其他常用多标记学习算法。     

Towards a unified multi-source-based optimization framework for multi-label learning

In the era of Big Data, a practical yet challenging task is to make learning techniques more universally applicable in dealing with the complex learning problem, such as multi-source multi-label learning. While some of the early work have developed many effective solutions for multi-label classification and multi-source fusion separately, in this paper we learn the two problems together, and propose a novel method for the joint learning of multiple class labels and data sources, in which an optimization framework is constructed to formulate the learning problem, and the result of multi-label classification is induced by the weighted combination of the decisions from multiple sources. The proposed method is responsive in exploiting the label correlations and fusing multi-source data, especially in the fusion of long-tail data. Experiments on various multi-source multi-label data sets reveal the advantages of the proposed method.     

多标签文本分类研究进展

文本分类作为自然语言处理中一个基本任务,在20世纪50年代就已经对其算法进行了研究,现在单标签文本分类算法已经趋向成熟,但是对于多标签文本分类的研究还有很大的提升空间。介绍了多标签文本分类的基本概念以及基本流程,包括数据集获取、文本预处理、模型训练和预测结果。介绍了多标签文本分类的方法。这些方法主要分为两大类：传统机器学习方法和基于深度学习的方法。传统机器学习方法主要包括问题转换方法和算法自适应方法。基于深度学习的方法是利用各种神经网络模型来处理多标签文本分类问题,根据模型结构,将其分为基于CNN结构、基于RNN结构和基于Transfomer结构的多标签文本分类方法。对多标签文本分类常用的数据集进行了梳理总结。对未来的发展趋势进行了分析与展望。     

Efficient max-margin multi-label classification with applications to zero-shot learning

The goal in multi-label classification is to tag a data point with the subset of relevant labels from a pre-specified set. Given a set of L labels, a data point can be tagged with any of the 2 ^L possible subsets. The main challenge therefore lies in optimising over this exponentially large label space subject to label correlations. Our objective, in this paper, is to design efficient algorithms for multi-label classification when the labels are densely correlated. In particular, we are interested in the zero-shot learning scenario where the label correlations on the training set might be significantly different from those on the test set. We propose a max-margin formulation where we model prior label correlations but do not incorporate pairwise label interaction terms in the prediction function. We show that the problem complexity can be reduced from exponential to linear while modelling dense pairwise prior label correlations. By incorporating relevant correlation priors we can handle mismatches between the training and test set statistics. Our proposed formulation generalises the effective 1-vs-All method and we provide a principled interpretation of the 1-vs-All technique. We develop efficient optimisation algorithms for our proposed formulation. We adapt the Sequential Minimal Optimisation (SMO) algorithm to multi-label classification and show that, with some book-keeping, we can reduce the training time from being super-quadratic to almost linear in the number of labels. Furthermore, by effectively re-utilizing the kernel cache and jointly optimising over all variables, we can be orders of magnitude faster than the competing state-of-the-art algorithms. We also design a specialised algorithm for linear kernels based on dual co-ordinate ascent with shrinkage that lets us effortlessly train on a million points with a hundred labels.     

基于负相关性增强的不平衡多标签学习算法

由于标签空间过大,标签分布不平衡问题在多标签数据集中广泛存在,解决该问题在一定程度上可以提高多标签学习的分类性能。通过标签相关性提升分类性能是解决该问题的一种最常见的有效策略,众多学者进行了大量研究,然而这些研究更多地是采用基于正相关性策略提升性能。在实际问题中,除了正相关性外,标签的负相关性也可能存在,如果在考虑正相关性的同时,兼顾负相关性,无疑能够进一步改善分类器的性能。基于此,提出了一种基于负相关性增强的不平衡多标签学习算法——MLNCE,旨在解决多标签不平衡问题的同时,兼顾标签间的正负相关性,从而提高多标签分类器的分类性能。首先利用标签密度信息改造标签空间;然后在密度标签空间中探究标签真实的正反相关性信息,并添加到分类器目标函数中;最后利用加速梯度下降法求解输出权重以得到预测结果。在11个多标签标准数据集上与其他6种多标签学习算法进行对比实验,结果表明MLNCE算法可以有效提高分类精度。     

Bayesian multi-instance multi-label learning using Gaussian process prior

Multi-instance multi-label learning (MIML) is a newly proposed framework, in which the multi-label problems are investigated by representing each sample with multiple feature vectors named instances. In this framework, the multi-label learning task becomes to learn a many-to-many relationship, and it also offers a possibility for explaining why a concerned sample has the certain class labels. The connections between instances and labels as well as the correlations among labels are equally crucial information for MIML. However, the existing MIML algorithms can rarely exploit them simultaneously. In this paper, a new MIML algorithm is proposed based on Gaussian process. The basic idea is to suppose a latent function with Gaussian process prior in the instance space for each label and infer the predictive probability of labels by integrating over uncertainties in these functions using the Bayesian approach, so that the connection between instances and every label can be exploited by defining a likelihood function and the correlations among labels can be identified by the covariance matrix of the latent functions. Moreover, since different relationships between instances and labels can be captured by defining different likelihood functions, the algorithm may be used to deal with the problems with various multi-instance assumptions. Experimental results on several benchmark data sets show that the proposed algorithm is valid and can achieve superior performance to the existing ones.     

混合局部因果结构学习

局部因果结构学习是发现和学习给定一个目标变量的直接原因和直接结果而无需学习一个完整因果网络的过程。目前已有算法通常由两个步骤完成:步骤1使用约束类算法利用独立性测试学习目标变量的马尔科夫毯(MB)或父子节点集(PC),但是该步骤由于受到有限的数据样本量等因素影响使得独立性测试存在一定的错误性,而导致该步骤精度通常不是很高;步骤2利用V结构及Meek规则来进行边的定向,但是该步骤由于极其依赖于V结构的发现且同样受到有限样本的影响,使得算法精度相对不是很高。基于上述问题,提出利用打分和限制相结合的混合方式来缓减有限样本问题且提高算法精度。步骤1通过在基于限制的算法中加入打分思想来提高数据有效性,进而提出SIAPC算法;步骤2通过利用PC算法得到的定向结果和对部分数据集打分得到的定向结果的交集来确定边的方向,以此来降低对V结构的依赖性且缓减有限样本问题,之后使用独立性测试修正边的定向结果来进一步提高算法精度,进而提出HLCS算法。在标准贝叶斯网络上,实验验证了该算法相对于已有算法在精度方面具有更好的性能且能够有效缓减数据效率问题。     

基于局部标记信息的多标记学习算法

一个样例的标记信息可能会对附近其他样例的学习提供有用信息,特别是在数据比较匮乏的情况下,利用已标记数据与未标记数据间的相关性,能够在一定程度上避免因数据不足所造成的误差。针对样例之间的相关性研究,提出基于局部标记信息的多标记学习算法,算法首先获取样例的局部标记信息,然后将样例的局部标记信息引入属性空间构造新的样例集合,并根据新的样例集合进行分类。实验结果表明,算法的分类性能得到较大提升,且优于其他常用多标记学习算法。     

融合标记独有属性特征的k近邻多标记分类新算法

在多标记学习系统中,每个样本同时与多个类别标记相关,却均由一个属性特征向量描述。大部分已有的多标记分类算法采用的共同策略是使用相同的属性特征集合预测所有的类别标记,但它并非最佳选择,原因在于每个标记可能与其自身独有的属性特征相关性最大。针对这一问题,提出了融合标记独有属性特征的k近邻多标记分类算法—IML-kNN。首先对多标记数据的特征向量进行预处理,分别为每类标记构造对该类标记最具有判别能力的属性特征;然后基于得到的属性特征使用改进后的ML-kNN算法进行分类。实验结果表明,IML-kNN算法在yeast和image数据集上的性能明显优于ML-kNN算法以及其他3种常用的多标记分类算法。