Incorporating Label Co-Occurrence Into Neural Network-Based Models for Multi-Label Text Classification

Yao, Jiaqi; Wang, Keren; Yan, Jun

doi:10.1109/access.2019.2960626

Cited by 7 publications

(3 citation statements)

References 30 publications

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“…In [51] a deep learning based method has been introduced that incorporates second-order label-occurrence information into the network. The label-occurrence information is mapped into vectors multiplied by the feature vectors learned by the feature extractors.…”

Section: Related Workmentioning

confidence: 99%

Threshold-Learned CNN for Multi-Label Text Classification of Electronic Health Records

Yang,

Emmert-Streib

2023

IEEE Access

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Text data in the form of natural language is a valuable resource that contains domain-specific information applicable to various applications. An example are electronic health records (eHR) offering comprehensive insights into patients' health histories, enabling knowledge extraction for clinical diagnosis and treatment. In this paper, we study multi-label text classification (MLTC) of eHR data by introducing two novel MLTC methods based on a threshold-learned convolutional neural network (CNN). We conduct comprehensive comparisons with other multi-label models and binary relevance (BR). Importantly, we do not only optimize the architecture of multi-label classifiers but also of the baseline BR model. As a result, our findings indicate that the adaptive-threshold CNN (AT-CNN) and implicit-threshold CNN (IT-CNN) provide a favorable approximation of a binary CNN (B-CNN) with the added benefit of improved runtime efficiency. The latter is crucial when the number of classes grows larger because the runtime of classifiers based on one-vs-rest mappings becomes increasingly prohibitive for such configurations.

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Section: Related Workmentioning

confidence: 99%

Threshold-Learned CNN for Multi-Label Text Classification of Electronic Health Records

Yang,

Emmert-Streib

2023

IEEE Access

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“…e multilayer perceptron classifier (MLPC) is a classification algorithm that is dependent on the framework. MLPC is utilizing backpropagation for learning [51]. e amount of hubs in the yield layer identifies with the number of classes.…”

Section: Classificationmentioning

confidence: 99%

NNPCov19: Artificial Neural Network-Based Propaganda Identification on Social Media in COVID-19 Era

Khanday

Bhushan

Jhaveri

et al. 2022

Mobile Information Systems

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The latest trend of sharing information has evolved many concerns for the current researchers, which are working on computational social sciences. Online social network platforms have become a tool for sharing propagandistic information. This is being used as a lethal weapon in modern days to destabilize democracies and other political or religious events. The COVID-19 affected almost every corner of the world. Various propagandistic tweets were shared on Twitter during the peak time of COVID-19. In this paper, improved artificial neural network algorithm is proposed to classify tweets into propagandistic and nonpropagandistic class. The data are extracted using multiple ambiguous hashtags and are manually annotated into binary class. Hybrid feature engineering is being performed by combining “Term Frequency (TF)/Inverse Document Frequency (IDF),” “Bag of Words,” and Tweet Length. The proposed algorithm is compared with logistic regression, support vector machine, and multinomial Naive Bayes. Results showed that improved artificial neural network algorithm outperforms other machine learning algorithms by having 77.15% accuracy, 77% of recall, and 79% precision. In future, deep learning approaches like LSTM may be used for this classification task.

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“…The idea of many published transformation-based MLC methods is improving performance by exploiting dependencies among labels. [2][3][4][5][6][7] Generally, the study of dependence among labels may consider the impacts of different types of dependence, the "strength" of the dependence, how to use the dependence to achieve better performance and how to capture dependence.…”

Section: Introductionmentioning

confidence: 99%

An experimental framework for evaluating loss minimization in multi‐label classification via stochastic process

Mello

Varejão

Rodrigues

2021

Computational Intelligence

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One major challenge multi‐label classification faces, are the conditions for evaluating multi‐label algorithms. Simplistic experimental setups based on artificial data may not capture crucial situations for analyzing these algorithms. This article introduces an experimental framework for evaluating multi‐label algorithms by artificially generating the probabilistic label distributions. The proposed framework has the benefits of considering a wide variety of labels distributions, and enables users to simulate probability label distributions with a better control of the label dependence and the difficulty of the problem. An experimental study was conducted using the framework where new findings with respect to five methods, binary relevance, classifier chain, dependent binary relevance, calibrated label ranking by pairwise comparison and probabilistic classifier chain, were revealed. This framework will facilitate conducting new experimental studies for analysing the effects of changing label dependence and the difficulty of the problem on the performance of new multi‐label algorithms.

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Incorporating Label Co-Occurrence Into Neural Network-Based Models for Multi-Label Text Classification

Cited by 7 publications

References 30 publications

Threshold-Learned CNN for Multi-Label Text Classification of Electronic Health Records

Threshold-Learned CNN for Multi-Label Text Classification of Electronic Health Records

NNPCov19: Artificial Neural Network-Based Propaganda Identification on Social Media in COVID-19 Era

An experimental framework for evaluating loss minimization in multi‐label classification via stochastic process

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