Semi-Supervised Multi-Label Classification Through Topological Active Learning

In multi-label image classification, each image is always associated with multiple labels and labels are usually correlated with each other. The intrinsic relation among labels can definitely contribute to classifier training. However, most previous studies on active learning for multi-label image classification purely mine label correlation based on observed label distribution. They ignore the mapping relation between examples and their labels. This mapping relation also implicates label relationship. Ignoring the mapping relation leads to an uncomprehensive label correlation estimation and results in a bad performance for classification. In this paper, we propose a novel multi-label active learning with low-rank mapping for image classification, called LMMAL, to solve this issue. More precisely, we train a low-rank mapping matrix to signify the mapping relation between the feature space and the label space of a certain multi-label dataset. Using this low-rank mapping relation, we exploit a full label correlation. Subsequently, an effective sampling strategy is designed by integrating this potential information with uncertainty to select the most informative example-label pairs. In addition, we extend LMMAL with automatic labeling (denoted as AL-LMMAL) to further reduce the annotation workload of active learning. Empirical results demonstrate the effectiveness of our approaches.

Multilabel image classification has been a hot topic in the field of computer vision and image understanding in recent years. To achieve better classification performance with fewer labeled images, multilabel active learning is used for this scenario. Several active learning methods have been proposed for multilabel image classification. However, all of them assume that either all training images have complete labels or label correlations are given at the beginning. These two assumptions are unrealistic. In fact, it is very difficult to obtain complete labels for each example, in particular when the size of labels in a multilabel dataset is very large. Typically, only partial labels are available. This is one type of “weak label” problem. To solve this weak label problem inside multilabel active learning, this paper proposes a novel solution called AE-WLMAL. AE-WLMAL explores conditional label correlations on the weak label problem with the help of input features and then utilizes label correlations to construct a unified sampling strategy and evaluate the informativeness of each example-label pair in a multilabel dataset for active sampling. In addition, a pruning strategy is adopted to further improve its computation efficiency. Moreover, AE-WLAML exploits label correlations to infer labels for unlabeled images, which further reduces human labeling cost. Our experimental results on seven real-world datasets show that AE-WLMAL consistently outperforms existing approaches.

In this work, we study semi-supervised multi-label node classification problem in attributed graphs. Classic solutions to multi-label node classification follow two steps, first learn node embedding and then build a node classifier on the learned embedding. To improve the discriminating power of the node embedding, we propose a novel collaborative graph walk, named Multi-Label-Graph-Walk, to finely tune node representations with the available label assignments in attributed graphs via reinforcement learning. The proposed method formulates the multi-label node classification task as simultaneous graph walks conducted by multiple label-specific agents. Furthermore, policies of the label-wise graph walks are learned in a cooperative way to capture first the predictive relation between node labels and structural attributes of graphs; and second, the correlation among the multiple label-specific classification tasks. A comprehensive experimental study demonstrates that the proposed method can achieve significantly better multi-label classification performance than the state-of-the-art approaches and conduct more efficient graph exploration.

Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also hard to build a good model without diagnosing discriminative labels. Can we reduce the label costs and improve the ability to train a good model for multi-label learning simultaneously? Active learning addresses the less training samples problem by querying the most valuable samples to achieve a better performance with little costs. In multi-label active learning, some researches have been done for querying the relevant labels with less training samples or querying all labels without diagnosing the discriminative information. They all cannot effectively handle the outlier labels for the measurement of uncertainty. Since maximum correntropy criterion (MCC) provides a robust analysis for outliers in many machine learning and data mining algorithms, in this paper, we derive a robust multi-label active learning algorithm based on an MCC by merging uncertainty and representativeness, and propose an efficient alternating optimization method to solve it. With MCC, our method can eliminate the influence of outlier labels that are not discriminative to measure the uncertainty. To make further improvement on the ability of information measurement, we merge uncertainty and representativeness with the prediction labels of unknown data. It cannot only enhance the uncertainty but also improve the similarity measurement of multi-label data with labels information. Experiments on benchmark multi-label data sets have shown a superior performance than the state-of-the-art methods.

Although most machine learning methods rely on the availability of large sets of labeled training instances, most real-world available data is unlabeled, since labeling can be expensive and time-consuming. To avoid the expense of labeling all the data before fitting a model, the paradigm of active learning allows models to query the labeling of only the most relevant samples to its training. However, when active learning is performed online, specially with streaming data, updating the model can be challenging, as it usually requires running several optimization steps or inverting matrices to retrain a model. In fact, this can make learning unfeasible for systems with computational constraints, such as embedded hardware. In this context, the present work aims at enhancing active learning with Hebbian classifiers, which allow updating param- eters inexpensively to learn new samples or to remove outdated information. For this purpose, we employ a recently proposed kernel-based method for embedding data in a suitable way for Hebbian learning and combine it with a querying criterion based on the Perceptron Convergence Theorem. The resulting model, called HebbAL, is applied to 17 UCI benchmarks, presenting data to the model in a streaming fashion. Our approach achieved comparable performance to widely used active learning models, such as a Random Forest with uncertainty querying. Although HebbAL presents a slightly lower AUC on average, it queries less labels than the other models. Therefore, our novel approach to online active learning represents a step towards building scalable learning systems to act in dynamical environments.

Multi-label active learning for image classification has attracted great attention over recent years and a lot of relevant works are published continuously. However, there still remain some problems that need to be solved, such as existing multi-label active learning algorithms do not reflect on the cleanness of sample data and their ways on label correlation mining are defective. For one thing, sample data is usually contaminated in reality, which disturbs the estimation of data distribution and further hinders the model training. For another, previous approaches for label relationship exploration are purely based on the observed label distribution of an incomplete training set, which cannot provide sufficiently efficient information. To address these issues, we propose a novel adaptive low-rank multi-label active learning algorithm, called LRMAL. Specifically, we first use low-rank matrix recovery to learn an effective low-rank feature representation from the noisy data. In a subsequent sampling phase, we make use of its superiorities to evaluate the general informativeness of each unlabeled example-label pair. Based on an intrinsic mapping relation between the example space and the label space of a certain multi-label dataset, we recover the incomplete labels of a training set for a more comprehensive label correlation mining. Furthermore, to reduce the redundancy among the selected example-label pairs, we use a diversity measurement to diversify the sampled data. Finally, an effective sampling strategy is developed by integrating these two aspects of potential information with uncertainty based on an adaptive integration scheme. Experimental results demonstrate the effectiveness of our approach.

Most existing work on multi-label learning focused on supervised learning which requires manual annotation samples that is labor-intensive, time-consuming and costly. To address such a problem, we present a novel method that incorporates active learning into the semi-supervised learning for multi-label image classification. What’s more, aiming at the curse of dimensionality existing in high-dimensional data, we explore a dimensionality reduction technique with non-negative sparseness constraint to extract a group of features that can completely describe the data and hence make the learning model more efficiently. Experimental results on common data sets validate that the proposed algorithm is relatively effective to improve the performance of the learner in multi-label classification, and the obtained learner is with reliability and robustness after data dimensionality using NNS-DR (Non-Negative Sparseness for Dimensionality Reduction).

Extending version-space theory to multi-label active learning with imbalanced data

Margin-based active learning remains the most widely used active learning paradigm due to its simplicity and empirical successes. However, most works are limited to binary or multiclass prediction problems, thus restricting the applicability of these approaches to many complex prediction problems where active learning would be most useful. For example, machine learning techniques for natural language processing applications often require combining multiple interdependent prediction problems—generally referred to as learning in structured output spaces. In many such application domains, complexity is further managed by decomposing a complex prediction into a sequence of predictions where earlier predictions are used as input to later predictions—commonly referred to as a pipeline model. This work describes methods for extending existing margin-based active learning techniques to these two settings, thus increasing the scope of problems for which active learning can be applied. We empirically validate these proposed active learning techniques by reducing the annotated data requirements on multiple instances of synthetic data, a semantic role labeling task, and a named entity and relation extraction system.

Multi-label classification aims to associate multiple labels to a given data/object instance to better describe them. Multi-label data sets are common in a lot of emerging application areas like: Text/Multimedia classification, Bio-Informatics, Medical image annotations and Computer Vision to name a few. There is a growing interest in efficient and accurate multi-label classification. There are two major approaches to perform multi-label classification (i) problem transformation methods and (ii) algorithm adaptation methods. In algorithm adaptation, the traditional classification algorithms are modified to handle multi-label data sets. One classification algorithm which is often modified to do multi-label classification is k- nearest neighbor (kNN). k-nearest neighbor is popular due to its simplicity, easy to implement and seamlessly adaptability. Despite its merits it has several drawbacks like: sensitivity of noisy data, missing values and outliers; feature scaling and often becoming inaccurate for large overlapping solution space. In this paper, a modification to kNN method is suggested for multi-label classification with three improvement strategies (i) selection of local example w.r.t. unknown example – the motivation for this comes from the fact that local and relevant space is vital for the improvement in multi-label classification; (ii) Splitting the input space into multiple sub-spaces for optimal label estimation – the motivation is to estimate label accurately in the presence of noisy labels; And (iii) selection of labels using Mean Average Precision (MAP) estimates – here our motivation is to utilize the training data effectively to maximize the hidden distribution and optimal parameters for the method. The proposed method is implemented and compared with state-of-the-art approaches based on kNN or similar approaches that effectively select and optimize relevant spaces for multi-label classification. Evaluation based on multiple metrics like Hamming loss, Precision/Recall and F-measure are used for evaluation. The suggested approach performed much better than the state-of-the-art on the datasets with strong label cardinalities.

Fast Laplacian twin support vector machine with active learning for pattern classification

Active Learning is a machine learning and data mining technique that selects the most informative samples for labeling and uses them as training data; it is especially useful when there are large amount of unlabeled data and labeling them is expensive. Recently, batch-mode active learning, where a set of samples are selected concurrently for labeling, based on their collective merit, has attracted a lot of attention. The objective of batch-mode active learning is to select a set of informative samples so that a classifier learned on these samples has good generalization performance on the unlabeled data. Most of the existing batch-mode active learning methodologies try to achieve this by selecting samples based on certain criteria. In this article we propose a novel criterion which achieves good generalization performance of a classifier by specifically selecting a set of query samples that minimize the difference in distribution between the labeled and the unlabeled data, after annotation. We explicitly measure this difference based on all candidate subsets of the unlabeled data and select the best subset. The proposed objective is an NP-hard integer programming optimization problem. We provide two optimization techniques to solve this problem. In the first one, the problem is transformed into a convex quadratic programming problem and in the second method the problem is transformed into a linear programming problem. Our empirical studies using publicly available UCI datasets and two biomedical image databases demonstrate the effectiveness of the proposed approach in comparison with the state-of-the-art batch-mode active learning methods. We also present two extensions of the proposed approach, which incorporate uncertainty of the predicted labels of the unlabeled data and transfer learning in the proposed formulation. In addition, we present a joint optimization framework for performing both transfer and active learning simultaneously unlike the existing approaches of learning in two separate stages, that is, typically, transfer learning followed by active learning. We specifically minimize a common objective of reducing distribution difference between the domain adapted source, the queried and labeled samples and the rest of the unlabeled target domain data. Our empirical studies on two biomedical image databases and on a publicly available 20 Newsgroups dataset show that incorporation of uncertainty information and transfer learning further improves the performance of the proposed active learning based classifier. Our empirical studies also show that the proposed transfer-active method based on the joint optimization framework performs significantly better than a framework which implements transfer and active learning in two separate stages.

Multi label classification is concerned with learning from a set of instances that are associated with a set of labels, that is, an instance could be associated with multiple labels at the same time. This task occurs frequently in application areas like text categorization, multimedia classification, bioinformatics, protein function classification and semantic scene classification. Current multi-label classification methods could be divided into two categories. The first is called problem transformation methods, which transform multi-label classification problem into single label classification problem, and then apply any single label classifier to solve the problem. The second category is called algorithm adaptation methods, which adapt an existing single label classification algorithm to handle multi-label data. In this paper, we propose a multi-label classification approach based on correlations among labels that use both problem transformation methods and algorithm adaptation methods. The approach begins with transforming multi-label dataset into a single label dataset using least frequent label criteria, and then applies the PART algorithm on the transformed dataset. The output of the approach is multi-labels rules. The approach also tries to get benefit from positive correlations among labels using predictive Apriori algorithm. The proposed approach has been evaluated using two multi-label datasets named (Emotions and Yeast) and three evaluation measures (Accuracy, Hamming Loss, and Harmonic Mean). The experiments showed that the proposed approach has a fair accuracy in comparison to other related methods.

Multi-label learning is an effective framework for learning with objects that have multiple semantic labels, and has been successfully applied into many real-world tasks. In contrast with traditional single-label learning, the cost of labeling a multi-label example is rather high, thus it becomes an important task to train an effectivemulti-label learning model with as few labeled examples as possible. Active learning, which actively selects the most valuable data to query their labels, is the most important approach to reduce labeling cost. In this paper, we propose a novel approach MADM for batch mode multi-label active learning. On one hand, MADM exploits representativeness and diversity in both the feature and label space by matching the distribution between labeled and unlabeled data. On the other hand, it tends to query predicted positive instances, which are expected to be more informative than negative ones. Experiments on benchmark datasets demonstrate that the proposed approach can reduce the labeling cost significantly.

Recently, multilabel classification algorithms play an increasingly significant role in data mining and machine learning. However, some existing mutual information-based algorithms ignore the influence of the proportions of labels on the correlation degree between features and label sets. Besides, the correlation degree of label sets cannot be accurately measured in most traditional ReliefF algorithms, and the repeated calculation arises from the division of heterogeneous neighbors. To overcome these shortcomings, this paper proposes a multilabel feature selection method using mutual information and improved multilabel ReliefF (ML-ReliefF). First, the proportion of each label is calculated in label space and combined with the mutual information of features and labels to construct a novel correlation degree between features and label sets to preprocess multilabel datasets, which is used to reduce runtime of ML-ReliefF. Second, the mutual information of label sets is introduced into improving accuracy of the correlation degree among label sets. Furthermore, two types of correlation degree for label sets based on ML-ReliefF are developed to divide similar and heterogeneous samples more clearly. Third, a divided method of heterogeneous neighbors is presented to effectively avoid the repeated calculation in ML-ReliefF, and a novel method of feature weighting based on ML-ReliefF is constructed to evaluate the importance of features. Finally, a multilabel feature selection algorithm based on mutual information and ML-ReliefF for multilabel classification is designed to improve the performance of multilabel classification. Experiments under fourteen multilabel datasets show the effectiveness of our algorithm and improve the classification performance for multilabel datasets.