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positive and unlabeled learning on generating strategy for weakly anomaly detection

SIGNAL IMAGE AND VIDEO PROCESSING 2025年第1期19卷 1-11页

作者： Deng, Shizhuo Han, Bowen Li, Xiaohong Lan, Siqi Chen, Dongyue Jia, Tong Wang, Hao Northeastern Univ Coll Informat Sci & Engn Wenhua Rd Shenyang 110819 Liaoning Peoples R China Northeastern Univ Foshan Grad Sch Innovat Panpu Rd Foshan 528311 Guangdong Peoples R China Northeastern Univ Natl Frontiers Sci Ctr Ind Intelligence & Syst Opt Wenhua Rd Shenyang 110819 Liaoning Peoples R China

Anomalies are rare, contextual, and hard to annotate in anomaly detection scenarios. Usually, anomalies are coarse-grained labeled and there exists at least one abnormal patch in image and video segmentation. In such a setting, anomaly detection encounters anomaly diversity, quantity, and weakly-label problems. In this work, we propose a weakly anomaly detection framework by using positive-unlabeled learning (PUL) on generated weakly-labeled samples. This framework consists of sample generation module and anomaly detection module. In sample generation, Wasserstein Generative Adversarial Networks (WGAN) generates unlabeled patch-level samples including abnormal and normal classes. The unlabeled generated samples increase the quantity and diversity of anomalies for the PUL task. In anomaly detection model, a coarse-grained two-stage iterative PUL algorithm is proposed on positive labeled normal samples and unlabeled generated samples, which improves accuracy and stability. In the experiment, we evaluate our proposal in ablation study, which verifies the effectiveness of combining WGAN and PUL. Compared with typical anomaly detection models, our framework performs better with enhancements of 1.09% and 0.8% on the UCSD-Ped2 and Avenue datasets.

关键词： Video anomaly detection positive and unlabeled learning Generative adversarial networks Weakly supervised framework

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BiCSA-PUL: binary crow search algorithm for enhancing positive and unlabeled learning

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1729-1743页

作者： Azizi, Nabil Ben Othmane, Mohamed Hamouma, Moumen Siam, Abderrahim Haouassi, Hichem Ledmi, Makhlouf Hamdi-Cherif, Aboubekeur Department of Computer Science Mostefa Ben Boulaid University Batna 2 Batna 05001 Algeria Department of Computer Science Abbes Laghrour University Khenchela 40004 Algeria Knowledge Engineering and IT Security Laboratory (ICOSI) Abbes Laghrour University Khenchela 40004 Algeria Department of Computer Science Ferhat Abbas University of Setif 1 Setif 19137 Algeria

This paper presents a novel metaheuristic binary crow search algorithm (CSA) designed for positive-unlabeled (PU) learning, a paradigm where only positive and unlabeled data are available, with applications in many diversified fields, such as medical diagnosis and fraud detection. The algorithm represent a useful adaptation of CSA, itself inspired by the food-hiding behavior of crows. The proposed BiCSA-PUL (binary crow search algorithm for positive-unlabeled learning) selects reliable negative samples from unlabeled data using binary vectors, and updates positions employing Hamming distance, guided by a modified F1-score, as fitness function. The algorithm was tested on 30 samples from 10 diverse datasets, outperforming seven state-of-the-art PU learning methods. The results reveal that BiCSA-PUL provides a robust and efficient approach for PU learning, significantly improving fitness and reliability. This work opens new avenues for applying metaheuristic optimization methods to challenging classification problems with limited labeled data. The main limitations are the potentially time-intensive process of parameters tuning and reliance on initial sampling. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Classification Machine learning Metaheuristic optimization positive and unlabeled learning Raw search algorithm Two-step algorithms

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positive and unlabeled learning for Mobile Application Traffic Classification

Positive and Unlabeled Learning for Mobile Application Traff...

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IEEE Military Communications Conference (MILCOM)

作者： Hussey, Jason Stone, Kerri Camp, Tracy Colorado Sch Mines Golden CO 80401 USA iCR Inc New York NY USA

ISBN: (纸本)9781665485340

Encrypted messaging applications are a type of social media that provide privacy for the contents of messages, but are susceptible to side-channel attacks that take advantage of information leaked through network traces. Mobile network traffic classification using machine learning (ML) is an effective technique to take advantage of this leaked information to reveal the presence of a specific application on a network but requires fully-labeled datasets that are difficult to obtain in the real-world. We show the ability of positive and unlabeled (PU) learning to increase situational awareness of a given network by revealing the presence of mobile applications (e.g., social media applications used for encrypted messaging, such as Signal), using a large unlabeled dataset coupled with a small amount of labeled data for a given application. We attain an average f1-score of 0.8708 when classifying an encrypted messaging application, Signal, and an average f1-score of 0.6497 across every application in our dataset. Our results demonstrate that a dataset of network traffic containing a single target mobile application of interest can be used to reveal the presence of that application in a larger, unlabeled dataset. In other words, this technique makes it possible to expand the situational awareness of which encrypted social messaging applications are in use on defense networks with only a small amount of labeled data.

关键词： positive and unlabeled learning Android applications traffic classification encrypted messaging social media

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Robust model selection for positive and unlabeled learning with constraints

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Science China(Information Sciences) 2022年第11期65卷 94-106页

作者： Tong WEI Hai WANG Weiwei TU Yufeng LI Department of Computer Science and Technology Nanjing University 4Paradigm Inc.

positive and unlabeled(PU) learning is the problem in which training data contains only PU samples. Although PU learning is widely used in real-world applications, its model selection remains challenging. Specifically, traditional model selection methods are often highly sensitive to the class prior as well as the data size, resulting in human overhead in hyperparameter optimization. In this paper, we present a method called ODE(robust model selection) for robust model selection in PU learning. Two novel model evaluators based on the integral probability metric and area under the curve, which are free of the class prior, are introduced, and a variance reduction method is further employed to improve the quality of model selection. In addition, we perform model selection under user-defined constraints and propose a fast halvingstyle searching algorithm to efficiently identify the most promising model configuration. Extensive empirical studies demonstrate that our proposed method performs more robustly and is more computationally efficient than many state-of-the-art methods.

关键词： positive and unlabeled learning model selection automated machine learning

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A new method for positive and unlabeled learning with privileged information

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APPLIED INTELLIGENCE 2022年第3期52卷 2465-2479页

作者： Liu, Bo Liu, Qian Xiao, Yanshan Guangdong Univ Technol Dept Automat Guangzhou 510006 Peoples R China Guangdong Univ Technol Dept Comp Sci Guangzhou 510006 Peoples R China

positive and unlabeled learning (PU learning) has been studied to address the situation in which only positive and unlabeled examples are available. Most of the previous work has been devoted to identifying negative examples from the unlabeled data, so that the supervised learning approaches can be applied to build a classifier. However, for the remaining unlabeled data, they either exclude them from the learning phase or force them to belong to a class, and this always limits the performance of PU learning. In addition, previous PU methods assume the training data and the testing data have the same features representations. However, we can always collect the features that the training data have while the test data do not have, these kinds of features are called privileged information. In this paper, we propose a new method, which is based on similarity approach for the problem of positive and unlabeled learning with privileged information (SPUPIL), which consists of two steps. The proposed SPUPIL method first conducts KNN method to generate the similarity weights and then the similarity weights and privileged information are incorporated to the learning model based on Ranking SVM to build a more accurate classifier. We also use the Lagrangian method to transform the original model into its dual problem, and solve it to obtain the classifier. Extensive experiments on the real data sets show that the performance of the SPUPIL is better than the state-of-the-art PU learning methods.

关键词： positive and unlabeled learning Ranking SVM Privilege information

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A new dictionary-based positive and unlabeled learning method

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APPLIED INTELLIGENCE 2021年第12期51卷 8850-8864页

作者： Liu, Bo Liu, Zhijing Xiao, Yanshan Guangdong Univ Technol Guangzhou 510006 Peoples R China Guangdong Univ Technol Dept Automat Guangzhou 510006 Peoples R China Guangdong Univ Technol Dept Comp Sci Guangzhou 510006 Peoples R China

positive and unlabeled learning (PU learning) is designed to solve the problem that we only utilize the labeled positive examples and the unlabeled examples to train a classifier. A variety of methods have been proposed to solve this problem by incorporating unlabeled examples into learning. However, many methods treat the original features as input in the training stage and then build the classifier. In this paper, by use of two-step strategy, a novel method with dictionary learning is proposed for PU learning, which is briefly called PUDL. The proposed method is done in two steps. Firstly, we extract reliable negative examples from unlabeled examples to form the negative class. We then utilize dictionary learning to construct the feature representation which can learn new features for the original input. Secondly, we propose RankSVM-based (Ranking Support Vector Machine) model to incorporate the positive class, extracted negative class and similarity weights into learning so as to improve performance of the classifier. The Lagrange multiplier method is applied to convert the original model into its dual form. In addition, we put forward an interactive optimization framework to optimize the proposed objective model and obtain the classifier. Finally, we verify the performance of PUDL via experiment and the results demostrate that PUDL performs better than state-of-the-art PU learning methods.

关键词： positive and unlabeled learning Dictionary learning

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A network-based positive and unlabeled learning approach for fake news detection

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MACHINE learning 2022年第10期111卷 3549-3592页

作者： de Souza, Mariana Caravanti Nogueira, Bruno Magalhaes Rossi, Rafael Geraldeli Marcacini, Ricardo Marcondes dos Santos, Brucce Neves Rezende, Solange Oliveira ICMC USP BR-13566590 Sao Carlos Brazil FACOM UFMS BR-79070900 Campo Grande MS Brazil CPTL UFMS BR-79613000 Tres Lagoas Brazil

Fake news can rapidly spread through internet users and can deceive a large audience. Due to those characteristics, they can have a direct impact on political and economic events. Machine learning approaches have been used to assist fake news identification. However, since the spectrum of real news is broad, hard to characterize, and expensive to label data due to the high update frequency, One-Class learning (OCL) and positive and unlabeled learning (PUL) emerge as an interesting approach for content-based fake news detection using a smaller set of labeled data than traditional machine learning techniques. In particular, network-based approaches are adequate for fake news detection since they allow incorporating information from different aspects of a publication to the problem modeling. In this paper, we propose a network-based approach based on positive and unlabeled learning by Label Propagation (PU-LP), a one-class and transductive semi-supervised learning algorithm that performs classification by first identifying potential interest and non-interest documents into unlabeled data and then propagating labels to classify the remaining unlabeled documents. A label propagation approach is then employed to classify the remaining unlabeled documents. We assessed the performance of our proposal considering homogeneous (only documents) and heterogeneous (documents and terms) networks. Our comparative analysis considered four OCL algorithms extensively employed in One-Class text classification (k-Means, k-Nearest Neighbors Density-based, One-Class Support Vector Machine, and Dense Autoencoder), and another traditional PUL algorithm (Rocchio Support Vector Machine). The algorithms were evaluated in three news collections, considering balanced and extremely unbalanced scenarios. We used Bag-of-Words and Doc2Vec models to transform news into structured data. Results indicated that PU-LP approaches are more stable and achieve better results than other PUL and OCL approaches

关键词： Fake news One-class learning positive and unlabeled learning Transductive semi-supervised learning Graph-based learning Document management Text processing

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A graph-based approach for positive and unlabeled learning

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INFORMATION SCIENCES 2021年 580卷 655-672页

作者： Carnevali, Julio Cesar Rossi, Rafael Geraldeli Milios, Evangelos Lopes, Alneu de Andrade Univ Sao Paulo Inst Math & Comp Sci Sao Paulo SP Brazil Univ Fed Mato Grosso do Sul Campo Grande MS Brazil Dalhousie Univ Halifax NS Canada

positive and unlabeled learning (PUL) uses unlabeled documents and a few positive documents for retrieving a set of "interest" documents from a text collection. Usually, PUL approaches are based on the vector space model. However, when dealing with semisupervised learning for text classification or information retrieval, graph-based approaches have been proved to outperform vector space model-based approaches. So, in this article, a graph-based approach for PUL is proposed: Label Propagation for positive and unlabeled learning (LP-PUL). The proposed framework consists of three steps: (i) building a similarity graph, (ii) identifying reliable negative documents, and (iii) performing label propagation to classify the remaining unlabeled documents as positive or negative. We carried out experiments to measure the impact of the different choices in each step of the proposed framework. We also demonstrated that the proposal surpasses the classification performance of other PUL (RC-SVM, PU-LP, and PE-PUC) or one-class learning (k-NN-based, kMeans-based, and Dense Autoencoder) algorithms in terms of F-1. Considering the best results of any algorithm used in the experimental evaluation, PU-PUL can improve the classification performance from 2%, when using only 1 labeled document, to 28%, when 30 labeled documents are employed. (C) 2021 Elsevier Inc. All rights reserved.

关键词： positive and unlabeled learning Text classification Label propagation Transductive learning Graph-based learning

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Debiased graph contrastive learning based on positive and unlabeled learning

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INTERNATIONAL JOURNAL OF MACHINE learning AND CYBERNETICS 2024年第6期15卷 2527-2538页

作者： Li, Zhiqiang Wang, Jie Liang, Jiye Shanxi Univ Sch Comp & Informat Technol Minist Educ Key Lab Computat Intelligence & Chinese Informat P Taiyuan 030006 Shanxi Peoples R China Taiyuan Univ Technol Key Lab Coal Sci & Technol Shanxi Prov Taiyuan 030024 Shanxi Peoples R China

Graph contrastive learning (GCL) is one of the mainstream techniques for unsupervised graph representation learning, which reduces the distance between positive pairs and increases the distance between negative pairs in the embedding space to obtain discriminative representation. However, most existing GCL methods mainly focus on graph augmentation for positive samples, while the effect of negative samples is less explored, and they regard all samples except anchors as negative samples, which may push away latent positive samples that belong to the same class as the anchor, thus introducing false negative samples. To this end, this paper proposes a novel framework called Debiased Graph Contrastive learning Based on positive and unlabeled learning (DGCL-PU). Firstly, in this framework, we cluster the nodes by using the K-means algorithm and then treat the samples that are the same as the anchor as positive samples and the others as unlabelled samples. In this way, PU learning can be applied to assign scores for samples to determine the propensity of becoming negative samples relative to the anchor point. Secondly, we integrate the similarity between samples and the negative propensity score of samples, thereby obtaining reasonable weights for negative samples. Finally, the weighted graph contrastive loss is designed to obtain more discriminative feature representations and alleviate the bias of false negative samples. Moreover, DGCL-PU is a general framework, and it can be embedded into most existing GCL methods to improve their performance. Experiments on multiple benchmark datasets demonstrate that our method achieves state-of-the-art performance on multiple downstream tasks, including graph node classification, node clustering, and link prediction.

关键词： Graph contrastive learning positive and unlabeled learning False negative samples Graph representation learning

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Multi-instance positive and unlabeled learning with bi-level embedding

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INTELLIGENT DATA ANALYSIS 2022年第3期26卷 659-678页

作者： Tang, Xijia Xu, Chao Luo, Tingjin Hou, Chenping Natl Univ Def Technol Dept Syst Sci Changsha Peoples R China

Multiple Instance learning (MIL) is a widely studied learning paradigm which arises from real applications. Existing MIL methods have achieved prominent performances under the premise of plenty annotation data. Nevertheless, sufficient labeled data is often unattainable due to the high labeling cost. For example, the task in web image identification is to find similar samples among a large size of unlabeled dataset through a small number of provided target pictures. This leads to a particular scenario of Multiple Instance learning with insufficient positive and superabundant unlabeled data (PU-MIL), which is a hot research topic in MIL recently. In this paper, we propose a novel method called Multiple Instance learning with Bi-level Embedding (MILBLE) to tackle PU-MIL problem. Unlike other PU-MIL method using only simple single-level mapping, the bi-level embedding strategy are designed to customize specific mapping for positive and unlabeled data. It ensures the characteristics of key instance are not erased. Moreover, the weighting measure adopted in positive data can extracts the uncontaminated information of true positive instances without interference from negative ones. Finally, we minimize the classification error loss of mapped examples based on class-prior probability to train the optimal classifier. Experimental results show that our method has better performance than other state-of-the-art methods.

关键词： Multi-instance learning positive and unlabeled learning bi-level embedding classification

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