Air pollution prediction via multi-label classification

A Bayesian network classifier can be used to estimate the probability of an air pollutant overcoming a certain threshold. Yet multiple predictions are typically required regarding variables which are stochastically dependent, such as ozone measured in multiple stations or assessed according to by different indicators. The common practice (independent approach) is to devise an independent classifier for each class variable being predicted; yet this approach overlooks the dependencies among the class variables. By appropriately modeling such dependencies one can improve the accuracy of the forecasts. We address this problem by designing a multi-label classifier, which simultaneously predict multiple air pollution variables. To this end we design a multi-label classifier based on Bayesian networks and learn its structure through structural learning. We present experiments in three different case studies regarding the prediction of PM2.5 and ozone. The multi-label classifier outperforms the independent approach, allowing to take better decisions.     

Multi-label feature selection based on neighborhood mutual information

Multi-label learning deals with data associated with a set of labels simultaneously. Like traditional single-label learning, the high-dimensionality of data is a stumbling block for multi-label learning. In this paper, we first introduce the margin of instance to granulate all instances under different labels, and three different concepts of neighborhood are defined based on different cognitive viewpoints. Based on this, we generalize neighborhood information entropy to fit multi-label learning and propose three new measures of neighborhood mutual information. It is shown that these new measures are a natural extension from single-label learning to multi-label learning. Then, we present an optimization objective function to evaluate the quality of the candidate features, which can be solved by approximating the multi-label neighborhood mutual information. Finally, extensive experiments conducted on publicly available data sets verify the effectiveness of the proposed algorithm by comparing it with state-of-the-art methods.     

基于多标签传播机制的蛋白质相互作用网络功能模块检测*

鉴于多标签传播算法在发现社会网络的社区结构研究上具有快速、高效的求解能力,提出融合多源蛋白质生物学知识的基于多标签传播机制的蛋白质相互作用(PPI)网络功能模块检测算法.首先,结合PPI网络功能信息和结构信息初始化节点的标签.然后,利用基因表达数据描述蛋白质间的共表达性,依据共表达性构建标签集合,从中选择标签以实现标签在节点间真实可靠的传播.最后,将具有相同标识符的节点划分到同一功能模块中,获得最终结果.实验表明文中算法不仅具有良好的时间性能,而且在检测精度上也具有一定的竞争性.     

Applying semi-supervised learning in hierarchical multi-label classification

In classification problems with hierarchical structures of labels, the target function must assign labels that are hierarchically organized and it can be used either for single-label (one label per instance) or multi-label classification problems (more than one label per instance). In parallel to these developments, the idea of semi-supervised learning has emerged as a solution to the problems found in a standard supervised learning procedure (used in most classification algorithms). It combines labelled and unlabelled data during the training phase. Some semi-supervised methods have been proposed for single-label classification methods. However, very little effort has been done in the context of multi-label hierarchical classification. Therefore, this paper proposes a new method for supervised hierarchical multi-label classification, called HMC-RAkEL. Additionally, we propose the use of semi-supervised learning, self-training, in hierarchical multi-label classification, leading to three new methods, called HMC-SSBR, HMC-SSLP and HMC-SSRAkEL. In order to validate the feasibility of these methods, an empirical analysis will be conducted, comparing the proposed methods with their corresponding supervised versions. The main aim of this analysis is to observe whether the semi-supervised methods proposed in this paper have similar performance of the corresponding supervised versions.     

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max–Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC’s ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.     

基于I2C距离和标记相关性的多标记场景分类

将改进的ML-I2C与基于标记相关性的方法结合,提出一种改进的多标记场景分类方法。首先提取所有图像的SURF特征,将每个类用一个特征集来表示;然后采用改进的I2C方法来计算待测图像与已知类之间的距离,根据距离进行标记排序;最后根据排序,利用标记相关性来预测待测图像的所有可能标记。实验结果表明,该方法对多标记场景分类的准确率较高。     

Multi-label core vector machine with a zero label

Multi-label core vector machine (Rank-CVM) is an efficient and effective algorithm for multi-label classification. But there still exist two aspects to be improved: reducing training and testing computational costs further, and detecting relevant labels effectively. In this paper, we extend Rank-CVM via adding a zero label to construct its variant with a zero label, i.e., Rank-CVMz, which is formulated as the same quadratic programming form with a unit simplex constraint and non-negative ones as Rank-CVM, and then is solved by Frank–Wolfe method efficiently. Attractively, our Rank-CVMz has fewer variables to be solved than Rank-CVM, which speeds up training procedure dramatically. Further, the relevant labels are effectively detected by the zero label. Experimental results on 12 benchmark data sets demonstrate that our method achieves a competitive performance, compared with six existing multi-label algorithms according to six indicative instance-based measures. Moreover, on the average, our Rank-CVMz runs 83 times faster and has slightly fewer support vectors than its origin Rank-CVM.     

Efficient region-aware large graph construction towards scalable multi-label propagation

With fast growing number of images on photo-sharing websites such as Flickr and Picasa, it is in urgent need to develop scalable multi-label propagation algorithms for image indexing, management and retrieval. It has been well acknowledged that analysis in semantic region level may greatly improve image annotation performance compared to that in the holistic image level. However, region level approach increases the data scale to several orders of magnitude and proposes new challenges to most existing algorithms. In this work, we present a novel framework to effectively compute pairwise image similarity by accumulating the information of semantic image regions. Firstly, each image is encoded as Bag-of-Regions based on multiple image segmentations. Secondly, all image regions are separated into buckets with efficient locality-sensitive hashing (LSH) method, which guarantees high collision probabilities for similar regions. The k-nearest neighbors of each image and the corresponding similarities can be efficiently approximated with these indexed patches. Lastly, the sparse and region-aware image similarity matrix is fed into the multi-label extension of the entropic graph regularized semi-supervised learning algorithm [1]. In combination they naturally yield the capability of handling large-scale dataset. Extensive experiments on NUS-WIDE (260k images) and COREL-5k datasets validate the effectiveness and efficiency of our proposed framework for region-aware and scalable multi-label propagation.     

基于SVM期望间隔的多标签分类的主动学习

分类是数据挖掘领域研究中的核心技术之一。得到一个性能良好的分类器需要大量的训练样本,而对样本进行标记是一个十分消耗资源的过程,对多标签样本进行标记就更加困难。为了尽可能降低标记样本的成本,需要找出最能代表类别信息的样本。在基于SVM的分类方法中,分类器间隔越大,分类的精度就会越差。提出了一种基于期望间隔的主动学习方法,即依据当前分类器,选择最快缩小分类间隔的样本。通过实验证明,基于期望间隔的学习策略比基于决策值以及基于后验概率的策略有着更好的学习效果。