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hierarchical multi-label classification model for science and technology news based on heterogeneous graph semantic enhancement

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2469-e2469页

作者： Cheng, Quan Cheng, Jingyi Chen, Jian Liu, Shaojun Fuzhou Univ Sch Econ & Management Fuzhou Fujian Peoples R China Fujian Key Lab Informat Network Fuzhou Fujian Peoples R China Fujian Inst Sci & Technol Informat Fuzhou Fujian Peoples R China

In the context of high-quality economic development, technological innovation has emerged as a fundamental driver of socio-economic progress. The consequent proliferation of science and technology news, which acts as a vital medium for disseminating technological advancements and policy changes, has attracted considerable attention from technology management agencies and innovation organizations. Nevertheless, online science and technology news has historically exhibited characteristics such as limited scale, disorderliness, and multi-dimensionality, which is extremely inconvenient for users of deep application. While single-label classification techniques can effectively categorize textual information, they face challenges in leading science and technology news classification due to a lack of a hierarchical knowledge framework and insufficient capacity to reveal knowledge integration features. This study proposes a hierarchical multi-label classification model for science and technology news, enhanced by heterogeneous graph semantics. The model captures multi-dimensional themes and hierarchical structural features within science and technology news through a hierarchical transmission module. It integrates graph convolutional networks to extract node information and hierarchical relationships from heterogeneous graphs, while also incorporating prior knowledge from domain knowledge graphs to address data scarcity. This approach enhances the understanding and classification capabilities of the semantics of science and technology news. Experimental results demonstrate that the model achieves precision, recall, and F1 scores of 84.21%, 88.89%, and 86.49%, respectively, significantly surpassing baseline models. This research presents an innovative solution for hierarchical multi-label classification tasks, demonstrating significant application potential in addressing data scarcity and complex thematic classification challenges.

关键词： hierarchical multi-label classification Graph convolutional neural network Knowledge graph Science and technology news

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hierarchical multi-label classification With Gene-Environment Interactions in Disease Modeling

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STATISTICS IN MEDICINE 2025年第3-4期44卷 e10330页

作者： Li, Jingmao Zhang, Qingzhao Ma, Shuangge Fang, Kuangnan Xu, Yaqing Xiamen Univ Sch Econ Dept Stat & Data Sci Xiamen Fujian Peoples R China Xiamen Univ Wang Yanan Inst Studies Econ Xiamen Peoples R China Yale Sch Publ Hlth Dept Biostat New Haven CT USA Shanghai Jiao Tong Univ Sch Med Sch Publ Hlth Shanghai Peoples R China

In biomedical studies, gene-environment (G-E) interactions have been demonstrated to have important implications for analyzing disease outcomes beyond the main G and main E effects. Many approaches have been developed for G-E interaction analysis, yielding important findings. However, hierarchical multi-label classification, which provides insightful information on disease outcomes, remains unexplored in G-E analysis literature. Moreover, unlabeled data are commonly observed in practical settings but omitted by many existing methods of hierarchical multi-label classification. In this study, we consider a semi-supervised scenario and develop a novel approach for the two-layer hierarchical response with G-E interactions. A two-step penalized estimation is then proposed using an efficient expectation-maximization (EM) algorithm. Simulation shows that it has superior performance in classification and feature selection. The analysis of The Cancer Genome Atlas (TCGA) data on lung cancer demonstrates the practical utility of the proposed method. Overall, this study can fill the important knowledge gap in G-E interaction analysis by providing a widely applicable framework for hierarchical multi-label classification of complex disease outcomes.

关键词： G-E interactions hierarchical multi-label classification high-dimensional data semi-supervised

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hierarchical multi-label classification with Missing Information for Benthic Habitat Imagery

Hierarchical Multi-Label Classification with Missing Informa...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xu, Isaac Misiuk, Benjamin Lowe, Scott C. Gillis, Martin Trappenberg, Thomas Brown, Craig J. Dalhousie Univ Fac Comp Sci Halifax NS Canada Mem Univ Newfoundland Dept Geog St John NF Canada Mem Univ Newfoundland Dept Earth Sci St John NF Canada Vector Inst Toronto ON Canada Dalhousie Univ Dept Oceanog Halifax NS Canada

ISBN: (纸本)9798350359329;9798350359312

In this work, we apply state-of-the-art selfsupervised learning techniques on a large dataset of seafloor imagery, BenthicNet, and study their performance for a complex hierarchical multi-label (HML) classification downstream task. In particular, we demonstrate the capacity to conduct HML training in scenarios where there exist multiple levels of missing annotation information, an important scenario for handling heterogeneous real-world data collected by multiple research groups with differing data collection protocols. We find that, when using smaller one-hot image label datasets typical of local or regional scale benthic science projects, models pre-trained with self-supervision on a larger collection of in-domain benthic data outperform models pre-trained on ImageNet. In the HML setting, we find the model can attain a deeper and more precise classification if it is pre-trained with self-supervision on indomain data. We hope this work can establish a benchmark for future models in the field of automated underwater image annotation tasks and can guide work in other domains with hierarchical annotations of mixed resolution.

关键词： machine learning computer vision hierarchical multi-label classification benthic mapping

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hierarchical multi-label classification with Partial labels and Unknown Hierarchy 23

Hierarchical Multi-Label Classification with Partial Labels ...

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32nd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Jo, Suhyeon Shin, DongHyeok Na, Byeonghu Jang, JoonHo Moon, Il-Chul Korea Adv Inst Sci & Technol Daejeon South Korea

ISBN: (纸本)9798400701245

hierarchical multi-label classification aims at learning a multi-label classifier from a dataset whose labels are organized into a hierarchical structure. To the best of our knowledge, we propose for the first time the problem of finding a multi-label classifier given a partially labeled hierarchical multi-label dataset. We also assume the situation where the classifier cannot access hierarchical information during training. This work proposes an iterative framework for learning both multi-labels and a hierarchical structure of classes. When training a multi-label classifier from partial labels, our model extracts a class hierarchy from the classifier output using our hierarchy extraction algorithm. Then, our proposed loss exploits the extracted hierarchy to train the classifier. Theoretically, we show that our hierarchy extraction algorithm correctly finds the unknown hierarchy under a mild condition, and we prove that our loss function of multi-label classification with such hierarchy becomes an unbiased estimator of true multi-label classification risk. Our experiments show that our model obtains a class hierarchy close to the ground-truth dataset hierarchy, and simultaneously, our method outperforms previous methods for hierarchical multi-label classification and multi-label classification from partial labels.

关键词： multi-label classification hierarchical multi-label classification multi-label learning from partial labels Weakly supervised learning Semi-supervised learning

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hierarchical multi-label classification of Library Subject Headings

Hierarchical Multi-Label Classification of Library Subject H...

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IEEE International Conference on Cybernetics and Innovations (ICCI)

作者： Wandee, Worrawan Songmuang, Pokpong Thammasat Univ Fac Sci & Technol Dept Comp Sci Bangkok Thailand

ISBN: (纸本)9781665486194

Assigning the subject heading is a significant work of the librarian. Subject heading represents the overall contents of the books which is a great way to easily find the books related directly to the user's needs. But a book can be related to more than one subject and Some librarians are not experts in every discipline. They take more time on this process. In this paper, our research has a small dataset, so the multi-label classification technique is unsuitable for this work. Therefore, we propose the hierarchical multi-label classification to help them assign subject heading to the book from a title and a table of contents. We also compare the performance of three techniques: Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), to select the best classification technique. According to the results, Random Forest technique is the best one for the first hierarchy classifiers with the highest F1 of 0.908. The second hierarchy classifiers that uses Random Forest technique attains the F1 score of 0.871.

关键词： hierarchical multi-label classification multilabel classification (MLC) Text classification Subject headings Library Subject Headings

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HmcNet: A General Approach for hierarchical multi-label classification

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第9期35卷 8713-8728页

作者： Huang, Wei Chen, Enhong Liu, Qi Xiong, Hui Huang, Zhenya Tong, Shiwei Zhang, Dan Univ Sci & Technol China Anhui Prov Key Lab Big Data Anal & Applicat State Key Lab Cognit Intelligence Hefei Peoples R China Hong Kong Univ Sci & Technol Guangzhou Hong Kong Peoples R China IFLYTEK Co Ltd iFLYTEK Res Hefei Anhui Peoples R China

hierarchical multi-label classification (HMC) deals with the problem of assigning each entity to multiple classes with a taxonomic structure (e.g., tree). Within this structure, classes at different levels tend to have dependencies under the hierarchy constraints. However, most prior studies for HMC tasks tend to ignore the class dependencies within the hierarchy. Moreover, most existing methods generate incoherent predictions and do not satisfy the hierarchy constraint. To this end, based on previously developed HARNN, we propose a general framework, HmcNet, for introducing explicit and implicit class hierarchy constraints to generate coherent predictions. We develop an efficient Prune-based Coherent Prediction (PCP) strategy for the optimal paths selection, which produces coherent predictions in a principled way. HmcNet can be well explained from two perspectives. First, it develops the hierarchical Attention-based Memory (HAM) unit with implicit class hierarchy constraints to capture class dependencies more intuitively;Second, it subsumes explicit class hierarchy constraints during training and inference phases and generates coherent predictions in a consistent manner. Finally, extensive experimental results on six real-world datasets demonstrate the effectiveness and interpretability of the HmcNet frameworks. To facilitate future research, our code has been made publicly available.

关键词： hierarchical multi-label classification attention mechanism path selection

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Error Detection and Constraint Recovery in hierarchical multi-label classification without Prior Knowledge 24

Error Detection and Constraint Recovery in Hierarchical Mult...

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33rd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Kricheli, Joshua Shay Vo, Khoa Datta, Aniruddha Ozgur, Spencer Shakarian, Paulo Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9798400704369

Recent advances in hierarchical multi-label classification (HMC), particularly neurosymbolic-based approaches, have demonstrated improved consistency and accuracy by enforcing constraints on a neural model during training. However, such work assumes the existence of such constraints a-priori. In this paper, we relax this strong assumption and present an approach based on Error Detection Rules (EDR) that allow for learning explainable rules about the failure modes of machine learning models. We show that these rules are not only effective in detecting when a machine learning classifier has made an error but also can be leveraged as constraints for HMC, thereby allowing the recovery of explainable constraints even if they are not provided. We show that our approach is effective in detecting machine learning errors and recovering constraints, is noise tolerant, and can function as a source of knowledge for neurosymbolic models on multiple datasets, including a newly introduced military vehicle recognition dataset.

关键词： Metacognitive AI Learning with Constraints Rule Learning NeuroSymbolic AI hierarchical multi-label classification

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BIT-WOW at NLPCC-2022 Task5 Track1: hierarchical multi-label classification via label-Aware Graph Convolutional Network 11th

BIT-WOW at NLPCC-2022 Task5 Track1: Hierarchical Multi-label...

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11th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC)

作者： Wang, Bo Lu, Yi-Fan Wei, Xiaochi Liu, Xiao Shi, Ge Yuan, Changsen Huang, Heyan Feng, Chong Mao, Xianling Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Baidu Inc Beijing Peoples R China Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Beijing Inst Technol Southeast Acad Informat Technol Beijing Peoples R China

ISBN: (纸本)9783031171895;9783031171888

This paper describes the system proposed by the BIT-WOW team for NLPCC2022 shared task in Task5 Track1. The track is about multi-label towards abstracts of academic papers in scientific domain, which includes hierarchical dependencies among 1,530 labels. In order to distinguish semantic information among hierarchical label structures, we propose the label-aware Graph Convolutional Network (LaGCN), which uses Graph Convolutional Network to capture the label association through context-based label embedding. Besides, curriculum learning is applied for domain adaptation and to mitigate the impact of a large number of categories. The experiments show that: 1) LaGCN effectively models the category information and makes a considerable improvement in dealing with a large number of categories;2) Curriculum learning is beneficial for a single model in the complex task. Our best results were obtained by an ensemble model. According to the official results, our approach proved the best in this track.

关键词： hierarchical multi-label classification Graph convolutional network Curriculum learning label embedding

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Towards ultrasonic guided wave fine-grained damage detection on hierarchical multi-label classification network

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2024年 218卷

作者： Guo, Ziye Zhou, Ruohua Gao, Yan Fu, Wei Yu, Qiuyu Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Exhibit Hall Rd 1 Beijing 102616 Peoples R China Chinese Acad Sci State Key Lab Acoust Inst Acoust Beijing 100190 Peoples R China

This study advocates the hierarchical multi-label classification (HMC) network for fine-grained damage detection using ultrasonic guided wave. Existing deep learning methods only focus on one aspect of the damage type or the degree of damage progression, and both are important for the long-term safe operation of the structure. To address this limitation, an ultrasonic guided wave HMC network (GHmcNet) and its lightweight version (L-GHmcNet) are developed. The essential designs of the proposed methods are derived from two motivations: (1) achieving robust fine-grained damage classification;(2) designing a lightweight network. The hierarchical labels (defect type-depth-size) are constructed on guided wave data. Inspired by the work of image classification, residual connections are introduced to transfer and fuse features between parent and children categories. The GHmcNet has three output channels and a weighted crossentropy loss function is imposed on each channel. The weights of the corresponding loss are automatically adjusted during training. To reduce the parameters, we improve the structure of GHmcNet by applying different numbers of convolutional layers and channels at different levels based on the number of classes in each level, proposing the lightweight model L-GHmcNet. Both numerical and experimental studies are carried out, in which the dataset contains signals of different damage types, depths and sizes. The results show the superiority of the proposed methods in terms of accuracy at three levels compared to state -of -the -art methods. Moreover, the high accuracy classification results of L-GHmcNet demonstrate that the proposed lightweight network is a successful attempt.

关键词： Guided waves Fine-grained hierarchical multi-label classification Defect detection Structural health monitoring

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Surj: Ontological Learning for Fast, Accurate, and Robust hierarchical multi-label classification 22

Surj: Ontological Learning for Fast, Accurate, and Robust Hi...

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31st ACM Web Conference (WWW)

作者： Yang, Sean T. Howe, Bill Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9781450391306

We consider multi-label classification in the context of complex hierarchical relationships organized into an ontology. These situations are ubiquitous in learning problems on the web and in science, where rich domain models are developed but labeled data is rare. Most existing solutions model the problem as a sequence of simpler problems: one classifier for each level in the hierarchy, or one classifier for each label. These approaches require more training data, which is often unavailable in practice: as the ontology grows in size and complexity, it becomes unlikely to find training examples for all expected combinations. In this paper, we learn offline representations of the ontology using a graph autoencoder and separately learn to classify input records, reducing dependence on training data: Since the relationships between labels are encoded independently of training data, the model can make predictions even for underrepresented labels, naturally generalize to DAG-structured ontologies, remain robust to low-data regimes, and, with minor offline retraining, tolerate evolving ontologies. We show empirically that our label predictions respect the hierarchy (predicting a descendant implies predicting its ancestors) and propose a method of evaluating hierarchy violations that properly ignores irrelevant violations. Our main result is that our model outperforms all state-of-the-art models on 17 of 20 datasets across multiple domains by a significant margin, even with limited training data.

关键词： hierarchical multi-label classification Graph Learning Ontology Learning

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