Adaptive Semi-supervised Learning Research Articles

Adaptive semi-supervised learning from stronger augmentation transformations of discrete text information

Adaptive semi-supervised learning from stronger augmentation transformations of discrete text information

CRAS: Curriculum Regularization and Adaptive Semi-Supervised Learning with Noisy Labels

This paper addresses the performance degradation of deep neural networks caused by learning with noisy labels. Recent research on this topic has exploited the memorization effect: networks fit data with clean labels during the early stages of learning and eventually memorize data with noisy labels. This property allows for the separation of clean and noisy samples from a loss distribution. In recent years, semi-supervised learning, which divides training data into a set of labeled clean samples and a set of unlabeled noisy samples, has achieved impressive results. However, this strategy has two significant problems: (1) the accuracy of dividing the data into clean and noisy samples depends strongly on the network’s performance, and (2) if the divided data are biased towards the unlabeled samples, there are few labeled samples, causing the network to overfit to the labels and leading to a poor generalization performance. To solve these problems, we propose the curriculum regularization and adaptive semi-supervised learning (CRAS) method. Its key ideas are (1) to train the network with robust regularization techniques as a warm-up before dividing the data, and (2) to control the strength of the regularization using loss weights that adaptively respond to data bias, which varies with each split at each training epoch. We evaluated the performance of CRAS on benchmark image classification datasets, CIFAR-10 and CIFAR-100, and real-world datasets, mini-WebVision and Clothing1M. The findings demonstrate that CRAS excels in handling noisy labels, resulting in a superior generalization and robustness to a range of noise rates, compared with the existing method.

XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly

Intrusion detection involves identifying unauthorized network activity and recognizing whether the data constitute an abnormal network transmission. Recent research has focused on using semi-supervised learning mechanisms to identify abnormal network traffic to deal with labeled and unlabeled data in the industry. However, real-time training and classifying network traffic pose challenges, as they can lead to the degradation of the overall dataset and difficulties preventing attacks. Additionally, existing semi-supervised learning research might need to analyze the experimental results comprehensively. This paper proposes XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANomaly, an image anomalous detection model that dynamically trains small subsets to these issues. First, this research introduces a deep neural network (DNN)-based GANomaly for semi-supervised learning. Second, this paper presents the proposed adaptive algorithm for the DNN-based GANomaly, which is validated with four subsets of the adaptive dataset. Finally, this study demonstrates a monitoring system that incorporates three explainable techniques—Shapley additive explanations, reconstruction error visualization, and t-distributed stochastic neighbor embedding—to respond effectively to attacks on traffic data at each feature engineering stage, semi-supervised learning, and adaptive learning. Compared to other single-class classification techniques, the proposed DNN-based GANomaly achieves higher scores for Network Security Laboratory-Knowledge Discovery in Databases and UNSW-NB15 datasets at 13% and 8% of F1 scores and 4.17% and 11.51% for accuracy, respectively. Furthermore, experiments of the proposed adaptive learning reveal mostly improved results over the initial values. An analysis and monitoring system based on the combination of the three explainable methodologies is also described. Thus, the proposed method has the potential advantages to be applied in practical industry, and future research will explore handling unbalanced real-time datasets in various scenarios.

XA-GANomaly: An Explainable Adaptive Semi-supervised Learning Method for Intrusion Detection using GANomaly in Global Economic dynamic shifts

Economic Intrusion detection is discovering unauthorized net-work activity and recognizing whether the data is an abnormal network transmission. Recent research has focused on semi-supervised learning mechanisms for identifying abnormal net-work traffic to deal with both labeled and unlabeled in industry. Economic shift generation is very crucial in creating a balance between hyper sensitive generation and superficial balance. However, real-time training and classifying network traffic remains as challenges, as they can lead to the degradation of the overall dataset and difficulties in preventing attacks. Addi-tionally, existing semi-supervised learning research may not comprehensively analyze the experimental results. To address these issues, we propose XA-GANomaly, a novel technique for explainable adaptive semi-supervised learning using GANoma-ly, an image anomalous detection model, that sequentially trains small subsets in a dynamic way. First, we introduce a Deep Neural Network (DNN) based GANomaly for semi-supervised learning. Second, we present our proposed adaptive algorithm for DNN-based GANomaly, validated with four sub-sets of the adaptive dataset. Finally, we demonstrate a monitor-ing system that incorporating three explainable techniques – SHapley Additive exPlanations (SHAP), reconstruction error visualization, and t-distributed stochastic neighbor embedding (t-SNE) – to respond effectively to attacks toward traffic data at each stage of feature engineering, semi-supervised learning, and adaptive learning. Compared to other single-class classifi-cation techniques, the proposed DNN-based GANomaly achieves higher scores than 13% and 8% of F1 scores and accuracy of 4.17% and 11.51% for the NSL-KDD and UNSW-NB15 datasets, respectively. In addition, our adaptive learning experiments show mostly improved results over initial values, and we provide an analysis and monitoring system based on the combination of the three explainable methodologies. As a result, our proposed method has the potential to be applied in the real industry, and future research will explore on handling unbalanced real-time datasets in various scenarios.The essence of such shifts in economic paradigm can be better understood by applying various triggers in functional machine to machine interaction.This can be better understood by creating a super-vised paradigm which better understands global economic shifts.

A Novel Semisupervised Regression Method for Online Total Nitrogen Detection Using Double Structural Sparse Feature Extraction

To solve the problem of insufficient labeled samples in online total nitrogen (TN) detection, a novel semi-supervised learning method called double regularized structure graph learning (DRSGL) was proposed, which can effectively extract useful features and support the TN detection equipment to establish accurate detection models with few labeled samples. Based on temporal and spectral informativity, a high-quality graph structure method was designed firstly, which utilizes the spectrum-temporal prior knowledge hidden in the spectrum and enhances the efficiency of the graph model. Then, considering the double sparsity of TN spectrum samples, a double structural sparse feature selection method (DS²FS) was invented accordingly, which can excavate useful information from both spectral and temporal dimensions. Finally, to address the insufficient problem of labeled samples, an adaptive semi-supervised learning method was constructed by combining graph learning strategy and DS²FS, and applied to the TN rapid detection prototype, which can iteratively select important features and update graph structure of samples correspondingly. According to the experimental results based on practical application, DRSGL can effectively solve the problem brought by the insufficiency of labeled samples, which can utilize only 20% labeled samples to establish an accurate detection model satisfied national detection standard with relative error lower than 10%.

RVFLN-based online adaptive semi-supervised learning algorithm with application to product quality estimation of industrial processes

Direct online measurement on product quality of industrial processes is difficult to be realized, which leads to a large number of unlabeled samples in modeling data. Therefore, it needs to employ semi-supervised learning (SSL) method to establish the soft sensor model of product quality. Considering the slow time-varying characteristic of industrial processes, the model parameters should be updated smoothly. According to this characteristic, this paper proposes an online adaptive semi-supervised learning algorithm based on random vector functional link network (RVFLN), denoted as OAS-RVFLN. By introducing a L2-fusion term that can be seen a weight deviation constraint, the proposed algorithm unifies the offline and online learning, and achieves smoothness of model parameter update. Empirical evaluations both on benchmark testing functions and datasets reveal that the proposed OAS-RVFLN can outperform the conventional methods in learning speed and accuracy. Finally, the OAS-RVFLN is applied to the coal dense medium separation process in coal industry to estimate the ash content of coal product, which further verifies its effectiveness and potential of industrial application.

ACoPE: An adaptive semi-supervised learning approach for complex-policy enforcement in high-bandwidth networks

Today’s high-bandwidth networks require adaptive analyzing approaches to recognize the network variable behaviors. The analyzing approaches should be robust against the lack of prior knowledge and provide data to impose more complex policies. In this paper, ACoPE is proposed as an adaptive semi-supervised learning approach for complex-policy enforcement in high-bandwidth networks. ACoPE detects and maintains inter-flows relationships to impose complex-policies. It employs a statistical process control technique to monitor accuracy. Whenever the accuracy decreased, ACoPE considers it as a changed behavior and uses data from a deep packet inspection module to adapt itself with the change.The performance of ACoPE in analyzing network traffic is evaluated through UNB ISCX VPN-nonVPN and UNB ISCX Tor-nonTor datasets. The performance is compared with 10 different stream and traditional classification algorithms. ACoPE outperforms the stream classifiers, with 95.92% accuracy, 86.21% precision, and 73.29% recall in VPN dataset, and with 81.12% accuracy, 73.59% precision, and 61.08% recall in Tor dataset. The effectiveness of ACoPE to address the main constraints in analyzing of high-bandwidth networks to enforce security policies, namely comprehensive processing and adaptive learning, are confirmed through three different scenarios. Efficiency and accuracy of ACoPE in real high-bandwidth networks are evaluated by a pilot study, which indicates its efficiency and accuracy in analyzing high-bandwidth networks.

RETRACTED ARTICLE: A novel semi supervised learning algorithm for thyroid and ulcer classification in tongue image

Tongue diagnosis is used to identify various diseases in the body now a day. The tongue image indicates the condition of different parts in body and signs in the tongue shows the misbehavior of internal parts of the body. This paper proposes a semi-supervised learning based approach to identify ulcer and thyroid of a patient by analyzing tongue image. The midpoint of tongue indicates intestine organ; if there is any problem in the intestine, it will be reflected in the midpoint of the tongue. Initially preprocessing steps are implemented to reduce noise and other irrelevant pixels from the image. Then segmentation is done to extract the specific part from the tongue and parameters collected from a healthy patient are considered to be the threshold value. The parameters collected from the extracted tongue image are compared with the threshold value, and the current condition of a patient is identified. A semi-supervised technique is introduced with a show the subtle elements of the patient whether he is in a protected or essential condition. The simulation work does with the MATLAB simulation environment utilizing adaptive semi-supervised learning technique. The simulation output shows the productivity of the semi-supervised learning strategy and its ability to recognize the issues of the tongues. Subsequently the proposed demonstrate delivers better highlights human body tissues. More than 97% efficiency accomplished by utilizing semi adaptive supervised learning technique.

Pertinent User Profile based on Adaptive Semi-supervised Learning

Several systems such as adaptive systems, etc. provide responses to the user by taking into account, among other, his profile. After each user-system interaction, new information should be added to the user profile content. By the time and after several updating operations, the profile can become overloaded and the removal of irrelevant content is necessary. In this paper, we tackle the profile overloading problem. We propose a new method based on co-training algorithm for detecting and removing irrelevant elements. Our method is automatically adapted to the content of any profile and allows us to obtain the most generic classifier to each one. An experimental study by qualitative and comparative evaluations shows that the proposed method can detect and remove irrelevant profile content effectively.

Adaptive semi-supervised learning on labeled and unlabeled data with different distributions

Developing methods for designing good classifiers from labeled samples whose distribution is different from that of test samples is an important and challenging research issue in the fields of machine learning and its application. This paper focuses on designing semi-supervised classifiers with a high generalization ability by using unlabeled samples drawn by the same distribution as the test samples and presents a semi-supervised learning method based on a hybrid discriminative and generative model. Although JESS-CM is one of the most successful semi-supervised classifier design frameworks based on a hybrid approach, it has an overfitting problem in the task setting that we consider in this paper. We propose an objective function that utilizes both labeled and unlabeled samples for the discriminative training of hybrid classifiers and then expect the objective function to mitigate the overfitting problem. We show the effect of the objective function by theoretical analysis and empirical evaluation. Our experimental results for text classification using four typical benchmark test collections confirmed that with our task setting in most cases, the proposed method outperformed the JESS-CM framework. We also confirmed experimentally that the proposed method was useful for obtaining better performance when classifying data samples into either known or unknown classes, which were included in given labeled samples or not, respectively.