Semi-supervised Classification Framework Research Articles

Pseudo Labeling Collaborative Embedding Representation: A Multi-Channel GCNs Framework for Semi-Supervised Node Classification

Pseudo Labeling Collaborative Embedding Representation: A Multi-Channel GCNs Framework for Semi-Supervised Node Classification

Self-Paced Co-Training of Graph Neural Networks for Semi-Supervised Node Classification.

Graph neural networks (GNNs) have demonstrated great success in many graph data-based applications. The impressive behavior of GNNs typically relies on the availability of a sufficient amount of labeled data for model training. However, in practice, obtaining a large number of annotations is prohibitively labor-intensive and even impossible. Co-training is a popular semi-supervised learning (SSL) paradigm, which trains multiple models based on a common training set while augmenting the limited amount of labeled data used for training each model via the pseudolabeled data generated from the prediction results of other models. Most of the existing co-training works do not control the quality of pseudolabeled data when using them. Therefore, the inaccurate pseudolabels generated by immature models in the early stage of the training process are likely to cause noticeable errors when they are used for augmenting the training data for other models. To address this issue, we propose a self-paced co-training for the GNN (SPC-GNN) framework for semi-supervised node classification. This framework trains multiple GNNs with the same or different structures on different representations of the same training data. Each GNN carries out SSL by using both the originally available labeled data and the augmented pseudolabeled data generated from other GNNs. To control the quality of pseudolabels, a self-paced label augmentation strategy is designed to make the pseudolabels generated at a higher confidence level to be utilized earlier during training such that the negative impact of inaccurate pseudolabels on training data augmentation, and accordingly, the subsequent training process can be mitigated. Finally, each of the trained GNN is evaluated on a validation set, and the best-performing one is chosen as the output. To improve the training effectiveness of the framework, we devise a pretraining followed by a two-step optimization scheme to train GNNs. Experimental results on the node classification task demonstrate that the proposed framework achieves significant improvement over the state-of-the-art SSL methods.

A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework

Deep learning has gained widespread interest in the task of building semantic segmentation modelling using remote sensing images; however, neural network models require a large number of training samples to achieve better classification performance, and the models are more sensitive to error patches in the training samples. The training samples obtained in semi-supervised classification methods need less reliable weakly labelled samples, but current semi-supervised classification research puts the generated weak samples directly into the model for applications, with less consideration of the impact of the accuracy and quality improvement of the weak samples on the subsequent model classification. Therefore, to address the problem of generating and optimising the quality of weak samples from training data in deep learning, this paper proposes a semi-supervised building classification framework. Firstly, based on the test results of the remote sensing image segmentation model and the unsupervised classification results of LiDAR point cloud data, this paper quickly generates weak image samples of buildings. Secondly, in order to improve the quality of the spots of the weak samples, an iterative optimisation strategy of the weak samples is proposed to compare and analyse the weak samples with the real samples and extract the accurate samples from the weak samples. Finally, the real samples, the weak samples, and the optimised weak samples are input into the semantic segmentation model of buildings for accuracy evaluation and analysis. The effectiveness of this paper’s approach was experimentally verified on two different building datasets, and the optimised weak samples improved by 1.9% and 0.6%, respectively, in the test accuracy mIoU compared to the initial weak samples. The results demonstrate that the semi-supervised classification framework proposed in this paper can be used to alleviate the model’s demand for a large number of real-labelled samples while improving the ability to utilise weak samples, and it can be used as an alternative to fully supervised classification methods in deep learning model applications that require a large number of training samples.

A Novel Robust Metric Distance Optimization-Driven Manifold Learning Framework for Semi-Supervised Pattern Classification

In this work, we address the problem of improving the classification performance of machine learning models, especially in the presence of noisy and outlier data. To this end, we first innovatively design a generalized adaptive robust loss function called Vθ(x). Intuitively, Vθ(x) can improve the robustness of the model by selecting different robust loss functions for different learning tasks during the learning process via the adaptive parameter θ. Compared with other robust loss functions, Vθ(x) has some desirable salient properties, such as symmetry, boundedness, robustness, nonconvexity, and adaptivity, making it suitable for a wide range of machine learning applications. Secondly, a new robust semi-supervised learning framework for pattern classification is proposed. In this learning framework, the proposed robust loss function Vθ(x) and capped L2,p-norm robust distance metric are introduced to improve the robustness and generalization performance of the model, especially when the outliers are far from the normal data distributions. Simultaneously, based on this learning framework, the Welsch manifold robust twin bounded support vector machine (WMRTBSVM) and its least-squares version are developed. Finally, two effective iterative optimization algorithms are designed, their convergence is proved, and their complexity is calculated. Experimental results on several datasets with different noise settings and different evaluation criteria show that our methods have better classification performance and robustness. With the Cancer dataset, when there is no noise, the classification accuracy of our proposed methods is 94.17% and 95.62%, respectively. When the Gaussian noise is 50%, the classification accuracy of our proposed methods is 91.76% and 90.59%, respectively, demonstrating that our method has satisfactory classification performance and robustness.

A multi-semantic passing framework for semi-supervised long text classification

A multi-semantic passing framework for semi-supervised long text classification

A Novel Framework for Semi-supervised Multiple-label Image Classification using Multi-stage CNN and Visual Attention Mechanism

To train deep neural networks effectively, a lot of labeled data is typically needed. However, real-time applications make it difficult and expensive to acquire high-quality labels for the data because it takes skill and knowledge to accurately annotate multiple label images. In order to enhance classification performance, it is also crucial to extract image features from all potential objects of various sizes as well as the relationships between labels of numerous label images. The current approaches fall short in their ability to map the label dependencies and effectively classify the labels. They also perform poor to label the unlabeled images when small amount of labeled images available for classification. In order to solve these issues, we suggest a new framework for semi-supervised multiple object label classification using multi-stage Convolutional neural networks with visual attention (MSCNN)and GCN for label co-occurrence embedding(LCE) (MSCNN-LCE-MIC), which combines GCN and attention mechanism to concurrently capture local and global label dependencies throughout the entire image classification process. Four main modules make up MSCNN-LCE-MIC: (1) improved multi-label propagation method for labeling largely available unlabeled image; (2) a feature extraction module using multi-stage CNN with visual attention mechanism that focuses on the connections between labels and target regions to extract accurate features from each input image; (3) a label co-existence learning that applies GCN to discover the associations between different items to create embeddings of label co-occurrence; and (4) an integrated multi-modal fusion module. Numerous tests on MS-COCO and PASCAL VOC2007 show that MSCNN-LCE-MIC significantly improves classification efficiency on mAP 84.3% and 95.8% respectively when compared to the most recent existing methods.

Semi-Supervised Neural Architecture Search for Hyperspectral Imagery Classification Method With Dynamic Feature Clustering

Hyperspectral image(HSI) contains rich spatial and spectral information, which makes HSI classification task the research focus of HSI analysis within remote sensing community. Though deep learning based HSI classification methods obtain good performance in recent years, how to learn network structure better suitable for a given HSI instead of utilizing a manually designed one for HSI classification is still a challenging problem, especially providing only small amount of labeled samples. To address this problem, we propose the first semi-supervised HSI classification network constructed via the neural architecture search. Specifically, we propose a two-head semi-supervised HSI classification framework utilizing both labeled and unlabeled data, which consists of a shared feature extraction module, a classifier module for labeled samples together with a clustering module for unlabeled samples. To boost the performance of the constructed two-head network, we propose to utilize deep features instead of the original pixels for HSI clustering to generate pseudo labels for the unlabeled data. Within the conducted semi-supervised network, we specifically design a method to automatically search for the shared feature extraction module better suitable for the given HSI data, which leads to better HSI classification results. Experimental results on three HSI datasets demonstrate the effectiveness of the proposed method, providing only limited number of labeled training samples.

IDN: inner-class dense neighbours for semi-supervised learning-based remote sensing scene classification

ABSTRACT In remote sensing scene classification, manual annotation is time-consuming and expensive to collect, which limits the effective deployment of supervised classification networks. Semi-supervised learning (SSL), which can benefit from unlabelled and labelled samples simultaneously, attracts much attention. In this letter, we introduce a cluster group construction method for semi-supervised high-resolution remote sensing (HRRS) scene classification. The low-density separation assumption, which assumes that the decision boundary of a classification network should lie in the low-density region, is a vital assumption to guide the construction of clusters. Following this, we propose to construct inner-class dense neighbours (IDN) for the semi-supervised classification framework. The proposed IDN can increase the density of clusters for a better determination of high-density regions to help the network training in the SSL framework. The experiments on the NWPU-RESIS45 and RSSCN7 datasets demonstrate the effectiveness of our proposed method, which show that the performance of the classification network with our proposed IDN has a prominent classification accuracy improvement.

Data augmentation based semi-supervised method to improve COVID-19 CT classification.

The Coronavirus (COVID-19) outbreak of December 2019 has become a serious threat to people around the world, creating a health crisis that infected millions of lives, as well as destroying the global economy. Early detection and diagnosis are essential to prevent further transmission. The detection of COVID-19 computed tomography images is one of the important approaches to rapid diagnosis. Many different branches of deep learning methods have played an important role in this area, including transfer learning, contrastive learning, ensemble strategy, etc. However, these works require a large number of samples of expensive manual labels, so in order to save costs, scholars adopted semi-supervised learning that applies only a few labels to classify COVID-19 CT images. Nevertheless, the existing semi-supervised methods focus primarily on class imbalance and pseudo-label filtering rather than on pseudo-label generation. Accordingly, in this paper, we organized a semi-supervised classification framework based on data augmentation to classify the CT images of COVID-19. We revised the classic teacher-student framework and introduced the popular data augmentation method Mixup, which widened the distribution of high confidence to improve the accuracy of selected pseudo-labels and ultimately obtain a model with better performance. For the COVID-CT dataset, our method makes precision, F1 score, accuracy and specificity 21.04%, 12.95%, 17.13% and 38.29% higher than average values for other methods respectively, For the SARS-COV-2 dataset, these increases were 8.40%, 7.59%, 9.35% and 12.80% respectively. For the Harvard Dataverse dataset, growth was 17.64%, 18.89%, 19.81% and 20.20% respectively. The codes are available at https://github.com/YutingBai99/COVID-19-SSL.

A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer's Disease Diagnosis.

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease that is not easily detectable in the early stage. This study proposed an efficient method of applying a graph convolutional network (GCN) on the early prediction of AD. We proposed a univariate neurodegeneration biomarker (UNB) based GCN semi-supervised classification framework. We generated UNB by comparing the similarity of individual morphological atrophy pattern and the atrophy pattern of [Formula: see text] AD group according to the brain morphological abnormalities induced by AD. For the GCN semi-supervised classification model, we took the UNBs of individuals as the features of nodes and constructed the weight of edges according to the similarity of phenotypic information between individuals, which explored the essential features of individuals through spectral graph convolution. The attention module was constructed and embedded into the GCN framework, which may refine the input morphological features to highlight the main impact of AD on the cerebral cortex and weaken the instability caused by individual diversities, thereby identifying the significant ROIs affected by AD and improving the classification accuracy. We tested the UNB-GCN framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The estimated minimum sample sizes were 156, 349 and 423 for the longitudinal [Formula: see text] AD, [Formula: see text] mild cognitive impairment (MCI) and [Formula: see text] cognitively unimpaired (CU) groups, respectively. And the proposed UNB-GCN framework combined with the attention module can effectively improve the classification performance with 93.90% classification accuracy for AD vs. CU and 82.05% for AD vs. MCI on the validation set. The proposed UNB measures were superior to the conventional volume measures in describing the AD-induced cerebral cortex morphological changes. And the UNB-GCN framework combined with attention module may effectively improve the classification performance between MCI subjects and AD patients. Clinical and Translational Impact Statement: This study aims to predict the early AD patients, so as to help clinicians develop effective interventions to delay the deterioration of AD symptoms.

Contrast-Enhanced Semi-supervised Text Classification with Few Labels

Traditional text classification requires thousands of annotated data or an additional Neural Machine Translation (NMT) system, which are expensive to obtain in real applications. This paper presents a Contrast-Enhanced Semi-supervised Text Classification (CEST) framework under label-limited settings without incorporating any NMT systems. We propose a certainty-driven sample selection method and a contrast-enhanced similarity graph to utilize data more efficiently in self-training, alleviating the annotation-starving problem. The graph imposes a smoothness constraint on the unlabeled data to improve the coherence and the accuracy of pseudo-labels. Moreover, CEST formulates the training as a “learning from noisy labels” problem and performs the optimization accordingly. A salient feature of this formulation is the explicit suppression of the severe error propagation problem in conventional semi-supervised learning. With solely 30 labeled data per class for both training and validation dataset, CEST outperforms the previous state-of-the-art algorithms by 2.11% accuracy and only falls within the 3.04% accuracy range of fully-supervised pre-training language model fine-tuning on thousands of labeled data.

Effectiveness of Semi-Supervised Learning and Multi-Source Data in Detailed Urban Landuse Mapping with a Few Labeled Samples

Detailed urban landuse information plays a fundamental role in smart city management. A sufficient sample size has been identified as a very crucial pre-request in machine learning algorithms for urban landuse classification. However, it is often difficult to recognize and label landuse categories from remote sensing images alone. Alternatively, field investigation is time-consuming with a high demand in human resources and monetary cost. Therefore, previous studies on urban landuse classification have often relied on a small size of labeled samples with very uneven spatial distribution. This study aims to explore the effectiveness of a semi-supervised classification framework with multi-source data for detailed urban landuse classification with a few labeled samples. A disagreement-based semi-supervised learning approach, the Co-Forest, was employed and compared with traditional supervised methods (e.g., random forest and XGBoost). Multi-source geospatial data were utilized including optical and nighttime light remote sensing and geospatial big data, which present the physical and socio-economic features of landuse categories. Taking urban landuse classification in Shenzhen City as a case, results show that the classification accuracy of the semi-supervised method are generally on par with that of traditional supervised methods, and less labeled samples are needed to achieve a comparable result under different training set ratios. Given a small sample size, the accuracy tends to be stable with training samples no less than 5% in total. Our results also indicate that the classification accuracy by using multi-source data is significantly higher than that with any single data source being applied. Among these data, map POI and high-resolution optical remote sensing data make larger contributions on the classification, followed by mobile data and nighttime light remote sensing data.

Graph Segmentation-Based Pseudo-Labeling for Semi-Supervised Pathology Image Classification

Pathology image classification is an important step in cancer diagnosis and precision treatment. Training a pathology image classification model in a fully supervised manner requires exhaustive pixel-level manual annotations from pathologists, which may not be practical in real applications. Semi-supervised learning (SSL) has been widely used to exploit large amounts of unlabeled data to facilitate model training with a small set of labeled data. However, due to the limited annotations, it still suffers from the issue of inaccurate pseudo-labels of unlabeled data. In this paper, we propose a novel framework for semi-supervised pathology image classification, which incorporates graph-based segmentation to refine initial pseudo-labels of tissue regions by considering local and global contextual relationships of patches in whole-slide images (WSIs). Moreover, we define a new energy function for graph construction that allows the graph to take into account the uncertainty of network predictions on unlabeled data. Extensive experiments on two different pathology image datasets demonstrate the effectiveness of our method compared with state-of-the-art SSL baselines. In particular, when using 5% labeled data, our approach outperforms a strong baseline by 2.81% AUC.

Graph convolutional and attention models for entity classification in multilayer networks

Graph Neural Networks (GNNs) are powerful tools that are nowadays reaching state of the art performances in a plethora of different tasks such as node classification, link prediction and graph classification. A challenging aspect in this context is to redefine basic deep learning operations, such as convolution, on graph-like structures, where nodes generally have unordered neighborhoods of varying size. State-of-the-art GNN approaches such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) work on monoplex networks only, i.e., on networks modeling a single type of relation among an homogeneous set of nodes. The aim of this work is to generalize such approaches by proposing a GNN framework for representation learning and semi-supervised classification in multilayer networks with attributed entities, and arbitrary number of layers and intra-layer and inter-layer connections between nodes. We instantiate our framework with two new formulations of GAT and GCN models, namely ML-GCN and ML-GAT, specifically devised for general, attributed multilayer networks. The proposed approaches are evaluated on an entity classification task on nine widely used real-world network datasets coming from different domains and with different structural characteristics. Results show that both our proposed ML-GAT and ML-GCN methods provide effective and efficient solutions to the problem of entity classification in multilayer attributed networks, being faster to learn and offering better accuracy than the competitors. Furthermore, results show how our methods are able to take advantage of the presence of real attributes for the entities, in addition to arbitrary inter-layer connections between the nodes in the various layers.

A Classification Method for Network Traffic Based on Semi-supervised Approach

Traffic data encryption has been a trend in most Internet applications, and traditional protocol filtering based on fixed port and traffic classification based on Deep Packet Inspection are unable to Identify encrypted traffic. Recently, the traffic classification method based on the statistical characteristics of network traffic, which can solve the problem of encrypting data or user privacy protection, has been widely discussed. However, the traditional supervised learning method requires manual marking of a large amount of network traffic data, which is tedious and time-consuming. In this paper, a improved semi-supervised traffic classification framework based on BIRCH clustering method is proposed, and through experiments, the proposed algorithm, supervised learning algorithm and classical semi-supervised traffic classification algorithm are analyzed and compared. The results show that the algorithm proposed in this paper has higher overall accuracy and classification accuracy, and the algorithm can increase the accuracy on traffic classification.

Improved Metric Learning With the CNN for Very-High-Resolution Remote Sensing Image Classification

The number of labeled samples has a great impact on the classification results of a very-high-resolution (VHR) remote sensing image. However, the acquisition of available labeled samples is difficult and time consuming. Faced with the limited labeled samples on a high-resolution remote sensing image, a semisupervised method becomes an effective way. In semisupervised learning, an accurate similarity prediction between unlabeled and labeled samples is very important. However, a reliable similarity prediction between high-dimensional features is difficult. For more reliable similarity prediction for the high-dimensional feature, a novel semisupervised classification framework via improved metric learning with a convolutional neural network is proposed. In the proposed method, a novel trainable metric learning network is designed to accurately evaluate the similarity between high-dimensional features. The vector distance parameter solving problem is transformed into a neural network design problem, which can automatically calculate parameters by the back-propagation algorithm. Finally, the pixel constraint mechanism is introduced to select the unlabeled samples. Experimental results conducted on three VHR remote sensing images, including Aerial, Xi'an, and Pavia University, and the results present that the proposed method performs better than the compared state-of-the-art methods.

Robust semi-supervised support vector machines with Laplace kernel-induced correntropy loss functions

The insufficiency and contamination of supervision information are the main factors affecting the performance of support vector machines (SVMs) in real-world applications. To address this issue, novel correntropy loss functions and Laplacian SVM (LapSVM) are utilized for robust semi-supervised classification. It is known that correntropy loss functions have been used in robust learning and achieved promising results. However, the potential for more diverse priors has not been extensively explored. In this paper, a correntropy loss function induced from Laplace kernel function, called LK-loss, is applied to LapSVM for the construction of robust semi-supervised classifier. Properties of LK-loss are demonstrated including robustness, symmetry, boundedness, Fisher consistency and asymptotic approximation behaviors. Moreover, the asymmetric version of LK-loss is introduced to further improve the performance. Concave-convex procedure (CCCP) technique is used to handle the non-convexity of Laplace kernel-induced correntropy loss functions iteratively. Experimental results show that in most cases, the proposed methods have better generalization performance than the comparing ones, which demonstrate the feasibility and effectiveness of the proposed semi-supervised classification framework.

Variational Information Bottleneck for Semi-Supervised Classification.

In this paper, we consider an information bottleneck (IB) framework for semi-supervised classification with several families of priors on latent space representation. We apply a variational decomposition of mutual information terms of IB. Using this decomposition we perform an analysis of several regularizers and practically demonstrate an impact of different components of variational model on the classification accuracy. We propose a new formulation of semi-supervised IB with hand crafted and learnable priors and link it to the previous methods such as semi-supervised versions of VAE (M1 + M2), AAE, CatGAN, etc. We show that the resulting model allows better understand the role of various previously proposed regularizers in semi-supervised classification task in the light of IB framework. The proposed IB semi-supervised model with hand-crafted and learnable priors is experimentally validated on MNIST under different amount of labeled data.

Pointwise manifold regularization for semi-supervised learning

Manifold regularization (MR) provides a powerful framework for semi-supervised classification using both the labeled and unlabeled data. It constrains that similar instances over the manifold graph should share similar classification outputs according to the manifold assumption. It is easily noted that MR is built on the pairwise smoothness over the manifold graph, i.e., the smoothness constraint is implemented over all instance pairs and actually considers each instance pair as a single operand. However, the smoothness can be pointwise in nature, that is, the smoothness shall inherently occur “everywhere” to relate the behavior of each point or instance to that of its close neighbors. Thus in this paper, we attempt to develop a pointwise MR (PW_MR for short) for semi-supervised learning through constraining on individual local instances. In this way, the pointwise nature of smoothness is preserved, and moreover, by considering individual instances rather than instance pairs, the importance or contribution of individual instances can be introduced. Such importance can be described by the confidence for correct prediction, or the local density, for example. PW_MR provides a different way for implementing manifold smoothness. Finally, empirical results show the competitiveness of PW_MR compared to pairwise MR.

Hyper-Heuristic Framework for Sequential Semi-Supervised Classification Based on Core Clustering

Existing stream data learning models with limited labeling have many limitations, most importantly, algorithms that suffer from a limited capability to adapt to the evolving nature of data, which is called concept drift. Hence, the algorithm must overcome the problem of dynamic update in the internal parameters or countering the concept drift. However, using neural network-based semi-supervised stream data learning is not adequate due to the need for capturing quickly the changes in the distribution and characteristics of various classes of the data whilst avoiding the effect of the outdated stored knowledge in neural networks (NN). This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). The framework trains the NN on previously labeled data and its knowledge is used to calculate the error of the core online-offline clustering block. The genetic optimization is responsible for selecting the best parameters of the core model to minimize the error. This integration aims to handle the concept drift. We designated this model as hyper-heuristic framework for semi-supervised classification or HH-F. Experimental results of the application of HH-F on real datasets prove the superiority of the proposed framework over the existing state-of-the art approaches used in the literature for sequential classification data with evolving nature.