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Journal articles on the topic 'Multi-label Text Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 June 2025

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1

Siringoringo, Rimbun, Jamaluddin Jamaluddin, and Resianta Perangin-angin. "TEXT MINING DAN KLASIFIKASI MULTI LABEL MENGGUNAKAN XGBOOST." METHOMIKA Jurnal Manajemen Informatika dan Komputerisasi Akuntansi 6, no. 6 (2022): 234–38. http://dx.doi.org/10.46880/jmika.vol6no2.pp234-238.

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The conventional classification process is applied to find a single criterion or label. The multi-label classification process is more complex because a large number of labels results in more classes. Another aspect that must be considered in multi-label classification is the existence of mutual dependencies between data labels. In traditional binary classification, classification analysis only aims to determine the label in the text, whether positive or negative. This method is sub-optimal because the relationship between labels cannot be determined. To overcome the weaknesses of these traditional methods, multi-label classification is one of the solutions in data labeling. With multi-label text classification, it allows the existence of many labels in a document and there is a semantic correlation between these labels. This research performs multi-label classification on research article texts using the ensemble classifier approach, namely XGBoost. Classification performance evaluation is based on several metrics criteria of confusion matrix, accuracy, and f1 score. Model evaluation is also carried out by comparing the performance of XGBoost with Logistic Regression. The results of the study using the train test split and cross-validation obtained an average accuracy of training and testing for Regression Logistics of 0.81, and an average f1 score of 0.47. The average accuracy for XGBoost is 0.88, and the average f1 score is 0.78. The results show that the XGBoost classifier model can be applied to produce a good classification performance.

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Wu, Tianxiang, and Shuqun Yang. "Contrastive Enhanced Learning for Multi-Label Text Classification." Applied Sciences 14, no. 19 (2024): 8650. http://dx.doi.org/10.3390/app14198650.

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Multi-label text classification (MLTC) aims to assign appropriate labels to each document from a given set. Prior research has acknowledged the significance of label information, but its utilization remains insufficient. Existing approaches often focus on either label correlation or label textual semantics, without fully leveraging the information contained within labels. In this paper, we propose a multi-perspective contrastive model (MPCM) with an attention mechanism to integrate labels and documents, utilizing contrastive methods to enhance label information from both textual semantic and correlation perspectives. Additionally, we introduce techniques for contrastive global representation learning and positive label representation alignment to improve the model’s perception of accurate labels. The experimental results demonstrate that our algorithm achieves superior performance compared to existing methods when evaluated on the AAPD and RCV1-V2 datasets.

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S. Tidake, Vaishali, and Shirish S. Sane. "Multi-label Classification: a survey." International Journal of Engineering & Technology 7, no. 4.19 (2018): 1045. http://dx.doi.org/10.14419/ijet.v7i4.19.28284.

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Wide use of internet generates huge data which needs proper organization leading to text categorization. Earlier it was found that a document describes one category. Soon it was realized that it can describe multiple categories simultaneously. This scenario reveals the use of multi-label classification, a supervised learning approach, which assigns a predefined set of labels to an object by looking at its characteristics. Earlier used in text categorization, but soon it became the choice of researchers for wide applications like marketing, multimedia annotation, bioinformatics. Two most common approaches for multi-label classification are transformation which takes the benefit of existing single label classifiers preceded by converting multi-label data to single label, or an adaptation which designs classifiers which handle multi-label data directly. Another popular approach is ensemble of multiple classifiers taking votes of all. Other approaches are also available namely algorithm independent and algorithm dependent approach. Based on results produced, suitable metric is used for example or label wise evaluation which depends on whether prediction is binary or ranking. Every approach offers benefits and issues like loss of label dependency in transformation, complexity in case of adaptation, improvement in results using ensemble which should be considered during design of underlying application.

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Abdullahi, Adeleke, Noor Azah Samsudin, Mohd Hisyam Abdul Rahim, Shamsul Kamal Ahmad Khalid, and Riswan Efendi. "Multi-label classification approach for Quranic verses labeling." Indonesian Journal of Electrical Engineering and Computer Science 24, no. 1 (2021): 484–90. https://doi.org/10.11591/ijeecs.v24.i1.pp484-490.

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Machine learning involves the task of training systems to be able to make decisions without being explicitly programmed. Important among machine learning tasks is classification involving the process of training machines to make predictions from predefined labels. Classification is broadly categorized into three distinct groups: single-label (SL), multi-class, and multi-label (ML) classification. This research work presents an application of a multi-label classification (MLC) technique in automating Quranic verses labeling. MLC has been gaining attention in recent years. This is due to the increasing amount of works based on real-world classification problems of multi-label data. In traditional classification problems, patterns are associated with a single-label from a set of disjoint labels. However, in MLC, an instance of data is associated with a set of labels. In this paper, three standard MLC methods: binary relevance (BR), classifier chain (CC), and label powerset (LP) algorithms are implemented with four baseline classifiers: support vector machine (SVM), naïve Bayes (NB), k-nearest neighbors (kNN), and J48. The research methodology adopts the multi-label problem transformation (PT) approach. The results are validated using six conventional performance metrics. These include: hamming loss, accuracy, one error, micro-F1, macro-F1, and avg. precision. From the results, the classifiers effectively achieved above 70% accuracy mark. Overall, SVM achieved the best results with CC and LP algorithms.

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5

Tandon, Kushagri, and Niladri Chatterjee. "Multi-label text classification with an ensemble feature space." Journal of Intelligent & Fuzzy Systems 42, no. 5 (2022): 4425–36. http://dx.doi.org/10.3233/jifs-219232.

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Multi-label text classification aims at assigning more than one class to a given text document, which makes the task more ambiguous and challenging at the same time. The ambiguities come from the fact that often several labels in the prescribed label set are semantically close to each other, making clear demarcation between them difficult. As a consequence, any Machine Learning based approach for developing multi-label classification scheme needs to define its feature space by choosing features beyond linguistic or semi-linguistic features, so that the semantic closeness between the labels is also taken into account. The present work describes a scheme of feature extraction where the training document set and the prescribed label set are intertwined in a novel way to capture the ambiguity in a meaningful way. In particular, experiments were conducted using Topic Modeling and Fuzzy C-Means clustering which aim at measuring the underlying uncertainty using probability and membership based measures, respectively. Several Nonparametric hypothesis tests establish the effectiveness of the features obtained through Fuzzy C-Means clustering in multi-label classification. A new algorithm has been proposed for training the system for multi-label classification using the above set of features.

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Sellah, Smail, and Vincent Hilaire. "Label Clustering for a Novel Problem Transformation in Multi-label Classification." JUCS - Journal of Universal Computer Science 26, no. (1) (2020): 71–88. https://doi.org/10.3897/jucs.2020.005.

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Document classification is a large body of search, many approaches were proposed for single label and multi-label classification. We focus on the multi-label classification more precisely those methods that transformation multi-label classification into single label classification. In this paper, we propose a novel problem transformation that leverage label dependency. We used Reuters-21578 corpus that is among the most used for text categorization and classification research. Results show that our approach improves the document classification at least by 8% regarding one-vs-all classification.

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Maruthupandi, J., and K. Vimala Devi. "Multi-label text classification using optimised feature sets." International Journal of Data Mining, Modelling and Management 9, no. 3 (2017): 237. http://dx.doi.org/10.1504/ijdmmm.2017.086583.

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Maruthupandi, J., and K. Vimala Devi. "Multi-label text classification using optimised feature sets." International Journal of Data Mining, Modelling and Management 9, no. 3 (2017): 237. http://dx.doi.org/10.1504/ijdmmm.2017.10007699.

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9

林, 娜. "Hierarchical Multi-label Text Classification Based on Bert." Advances in Applied Mathematics 13, no. 05 (2024): 2141–47. http://dx.doi.org/10.12677/aam.2024.135202.

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Zha, Daochen, and Chenliang Li. "Multi-label dataless text classification with topic modeling." Knowledge and Information Systems 61, no. 1 (2018): 137–60. http://dx.doi.org/10.1007/s10115-018-1280-0.

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Xiao, Lin, Xiangliang Zhang, Liping Jing, Chi Huang, and Mingyang Song. "Does Head Label Help for Long-Tailed Multi-Label Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (2021): 14103–11. http://dx.doi.org/10.1609/aaai.v35i16.17660.

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Multi-label text classification (MLTC) aims to annotate documents with the most relevant labels from a number of candidate labels. In real applications, the distribution of label frequency often exhibits a long tail, i.e., a few labels are associated with a large number of documents (a.k.a. head labels), while a large fraction of labels are associated with a small number of documents (a.k.a. tail labels). To address the challenge of insufficient training data on tail label classification, we propose a Head-to-Tail Network (HTTN) to transfer the meta-knowledge from the data-rich head labels to data-poor tail labels. The meta-knowledge is the mapping from few-shot network parameters to many-shot network parameters, which aims to promote the generalizability of tail classifiers. Extensive experimental results on three benchmark datasets demonstrate that HTTN consistently outperforms the state-of-the-art methods. The code and hyper-parameter settings are released for reproducibility.

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He, Zhiyang, Ji Wu, and Ping Lv. "Multi-label text classification based on the label correlation mixture model." Intelligent Data Analysis 21, no. 6 (2017): 1371–92. http://dx.doi.org/10.3233/ida-163055.

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Xu, Pengyu, Lin Xiao, Bing Liu, Sijin Lu, Liping Jing, and Jian Yu. "Label-Specific Feature Augmentation for Long-Tailed Multi-Label Text Classification." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10602–10. http://dx.doi.org/10.1609/aaai.v37i9.26259.

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Multi-label text classification (MLTC) involves tagging a document with its most relevant subset of labels from a label set. In real applications, labels usually follow a long-tailed distribution, where most labels (called as tail-label) only contain a small number of documents and limit the performance of MLTC. To facilitate this low-resource problem, researchers introduced a simple but effective strategy, data augmentation (DA). However, most existing DA approaches struggle in multi-label settings. The main reason is that the augmented documents for one label may inevitably influence the other co-occurring labels and further exaggerate the long-tailed problem. To mitigate this issue, we propose a new pair-level augmentation framework for MLTC, called Label-Specific Feature Augmentation (LSFA), which merely augments positive feature-label pairs for the tail-labels. LSFA contains two main parts. The first is for label-specific document representation learning in the high-level latent space, the second is for augmenting tail-label features in latent space by transferring the documents second-order statistics (intra-class semantic variations) from head labels to tail labels. At last, we design a new loss function for adjusting classifiers based on augmented datasets. The whole learning procedure can be effectively trained. Comprehensive experiments on benchmark datasets have shown that the proposed LSFA outperforms the state-of-the-art counterparts.

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Li, Bo. "BeNet: BERT Doc-Label Attention Network for Multi-Label Text Classification." Applied and Computational Engineering 54, no. 1 (2024): 171–83. http://dx.doi.org/10.54254/2755-2721/54/20241493.

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Multi-label Text Classification (MLTC) holds significant importance and serves as a foundational aspect in Natural Language Processing (NLP), which aims at assigning multiple labels for a given document. Many real-world tasks can be viewed as MLTC, such as tag recommendation, information retrieval, etc. Nevertheless, researchers are faced with numerous challenging issues regarding the establishment of linkages between labels or the differentiation of comparable sub-labels. To address this issue, we provide a novel approach known as the BERT Doc-Label Attention Network (BeNet) in this paper, which consist of the BERTdoc layer, the label embeddings layer, the doc encoder layer, the doc-label attention layer and the prediction layer. We apply the powerful technique of BERT to pretrain documents to capture their deep semantic features and encode them via Bi-LSTM to obtain a two-directional contextual representation of uniform length. Then we create label embeddings and feed them together with encoded-pretrained-documents to the doc-label attention mechanism to obtain interactive information between documents and their corresponding labels, finally using MLP to make predictions. We carry out experiments on two real-world datasets, and the empirical results demonstrate that our proposed model outperforms all state-of-the-art MLTC benchmarks. Furthermore, we have undertaken a case study to effectively illustrate the practical implementation of our method.

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Sellah, Smail, and Vincent Hilaire. "Label Clustering for a Novel Problem Transformation in Multi-label Classification." JUCS - Journal of Universal Computer Science 26, no. 1 (2020): 71–88. http://dx.doi.org/10.3897/jucs.2020.005.

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Abstract:

Document classification is a large body of search, many approaches were proposed for single label and multi-label classification. We focus on the multi-label classification more precisely those methods that transformation multi-label classification into single label classification. In this paper, we propose a novel problem transformation that leverage label dependency. We used Reuters-21578 corpus that is among the most used for text categorization and classification research. Results show that our approach improves the document classification at least by 8% regarding one-vs-all classification.

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16

Ruiz Alonso, Dorian, Claudia Zepeda Cortés, Hilda Castillo Zacatelco, José Luis Carballido Carranza, and José Luis García Cué. "Multi-label classification of feedbacks." Journal of Intelligent & Fuzzy Systems 42, no. 5 (2022): 4337–43. http://dx.doi.org/10.3233/jifs-219224.

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This work deals with educational text mining, a field of natural language processing applied to education. The objective is to classify the feedback generated by teachers in online courses to the activities sent by students according to the model of Hattie and Timperley (2007), considering that feedback may be at the levels task, process, regulation, praise and other. Four multi-label classification methods of the data transformation approach - binary relevance, classification chains, power labelset and rakel-d - are compared with the base algorithms SVM, Random Forest, Logistic Regression and Naive Bayes. The methodology was applied to a case study in which 11013 feedbacks written in Spanish language from 121 online courses of the Law degree from a public university in Mexico were collected from the Blackboard learning manager system. The results show that the random forests algorithms and vector support machines will have the best performance when using the binary relevance transformation and classifier chains methods.

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Liu, Huiting, Geng Chen, Peipei Li, Peng Zhao, and Xindong Wu. "Multi-label text classification via joint learning from label embedding and label correlation." Neurocomputing 460 (October 2021): 385–98. http://dx.doi.org/10.1016/j.neucom.2021.07.031.

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He, Sunan, Taian Guo, Tao Dai, et al. "Open-Vocabulary Multi-Label Classification via Multi-Modal Knowledge Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (2023): 808–16. http://dx.doi.org/10.1609/aaai.v37i1.25159.

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Real-world recognition system often encounters the challenge of unseen labels. To identify such unseen labels, multi-label zero-shot learning (ML-ZSL) focuses on transferring knowledge by a pre-trained textual label embedding (e.g., GloVe). However, such methods only exploit single-modal knowledge from a language model, while ignoring the rich semantic information inherent in image-text pairs. Instead, recently developed open-vocabulary (OV) based methods succeed in exploiting such information of image-text pairs in object detection, and achieve impressive performance. Inspired by the success of OV-based methods, we propose a novel open-vocabulary framework, named multi-modal knowledge transfer (MKT), for multi-label classification. Specifically, our method exploits multi-modal knowledge of image-text pairs based on a vision and language pre-training (VLP) model. To facilitate transferring the image-text matching ability of VLP model, knowledge distillation is employed to guarantee the consistency of image and label embeddings, along with prompt tuning to further update the label embeddings. To further enable the recognition of multiple objects, a simple but effective two-stream module is developed to capture both local and global features. Extensive experimental results show that our method significantly outperforms state-of-the-art methods on public benchmark datasets.

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Sharaff, Aakanksha, and Naresh Kumar Nagwani. "ML-EC2." International Journal of Web-Based Learning and Teaching Technologies 15, no. 2 (2020): 19–33. http://dx.doi.org/10.4018/ijwltt.2020040102.

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A multi-label variant of email classification named ML-EC2 (multi-label email classification using clustering) has been proposed in this work. ML-EC2 is a hybrid algorithm based on text clustering, text classification, frequent-term calculation (based on latent dirichlet allocation), and taxonomic term-mapping technique. It is an example of classification using text clustering technique. It studies the problem where each email cluster represents a single class label while it is associated with set of cluster labels. It is multi-label text-clustering-based classification algorithm in which an email cluster can be mapped to more than one email category when cluster label matches with more than one category term. The algorithm will be helpful when there is a vague idea of topic. The performance parameters Entropy and Davies-Bouldin Index are used to evaluate the designed algorithm.

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Huang, Shan, Wenlong Hu, Bin Lu, et al. "Application of Label Correlation in Multi-Label Classification: A Survey." Applied Sciences 14, no. 19 (2024): 9034. http://dx.doi.org/10.3390/app14199034.

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Multi-Label Classification refers to the classification task where a data sample is associated with multiple labels simultaneously, which is widely used in text classification, image classification, and other fields. Different from the traditional single-label classification, each instance in Multi-Label Classification corresponds to multiple labels, and there is a correlation between these labels, which contains a wealth of information. Therefore, the ability to effectively mine and utilize the complex correlations between labels has become a key factor in Multi-Label Classification methods. In recent years, research on label correlations has shown a significant growth trend internationally, reflecting its importance. Given that, this paper presents a survey on the label correlations in Multi-Label Classification to provide valuable references and insights for future researchers. The paper introduces multi-label datasets across various fields, elucidates and categorizes the concept of label correlations, emphasizes their utilization in Multi-Label Classification and associated subproblems, and provides a prospect for future work on label correlations.

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Wilges, Beatriz, Gustavo Mateus, Silvia Nassar, Renato Cislaghi, and Rogério Cid Bastos. "Fuzzy Modeling for Multi-Label Text Classification Supported by Classification Algorithms." Journal of Computer Science 12, no. 7 (2016): 341–49. http://dx.doi.org/10.3844/jcssp.2016.341.349.

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Ma, Yinglong, Xiaofeng Liu, Lijiao Zhao, Yue Liang, Peng Zhang, and Beihong Jin. "Hybrid embedding-based text representation for hierarchical multi-label text classification." Expert Systems with Applications 187 (January 2022): 115905. http://dx.doi.org/10.1016/j.eswa.2021.115905.

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Yuan, Ling, Xinyi Xu, Ping Sun, Hai ping Yu, Yin Zhen Wei, and Jun jie Zhou. "Research of multi-label text classification based on label attention and correlation networks." PLOS ONE 19, no. 9 (2024): e0311305. http://dx.doi.org/10.1371/journal.pone.0311305.

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Multi-Label Text Classification (MLTC) is a crucial task in natural language processing. Compared to single-label text classification, MLTC is more challenging due to its vast collection of labels which include extracting local semantic information, learning label correlations, and solving label data imbalance problems. This paper proposes a model of Label Attention and Correlation Networks (LACN) to address the challenges of classifying multi-label text and enhance classification performance. The proposed model employs the label attention mechanism for a more discriminative text representation and uses the correlation network based on label distribution to enhance the classification results. Also, a weight factor based on the number of samples and a modulation function based on prediction probability are combined to alleviate the label data imbalance effectively. Extensive experiments are conducted on the widely-used conventional datasets AAPD and RCV1-v2, and extreme datasets EUR-LEX and AmazonCat-13K. The results indicate that the proposed model can be used to deal with extreme multi-label data and achieve optimal or suboptimal results versus state-of-the-art methods. For the AAPD dataset, compared with the suboptimal method, it outperforms the second-best method by 2.05% ∼ 5.07% in precision@k and by 2.10% ∼ 3.24% in NDCG@k for k = 1, 3, 5. The superior outcomes demonstrate the effectiveness of LACN and its competitiveness in dealing with MLTC tasks.

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Omar, Ahmed, Tarek M. Mahmoud, Tarek Abd-El-Hafeez, and Ahmed Mahfouz. "Multi-label Arabic text classification in Online Social Networks." Information Systems 100 (September 2021): 101785. http://dx.doi.org/10.1016/j.is.2021.101785.

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Wen Li, Weili Wang, and Chaomei Zheng. "Multi-label Text Classification based on Minimum Decision Cost." International Journal of Digital Content Technology and its Applications 6, no. 19 (2012): 106–12. http://dx.doi.org/10.4156/jdcta.vol6.issue19.14.

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Štrimaitis, Rokas, Pavel Stefanovič, Simona Ramanauskaitė, and Asta Slotkienė. "A Combined Approach for Multi-Label Text Data Classification." Computational Intelligence and Neuroscience 2022 (June 22, 2022): 1–13. http://dx.doi.org/10.1155/2022/3369703.

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Automated data analysis solutions are very dependent on data and its quality. The possibility of assigning more than one class to the same data item is one of the specificities that need to be taken into account. There are no solutions, dedicated to Lithuanian text data classification that helps to assign more than one class to data item. In this paper, a new combined approach has been proposed for multilabel text data classification for text analysis. The main aim of the proposed approach is to improve the accuracy of traditional classification algorithms by incorporating the results obtained using similarity measures. The experimental investigation has been performed using the financial news multilabel text data in the Lithuanian language. Data have been collected from four public websites and classified by experts into ten classes manually, where each of the data items has no more than two classes. The results of five commonly used algorithms have been compared for dataset classification: the support vector machine, multinomial naive Bayes, k-nearest neighbours, decision trees, linear and discriminant analysis. In addition, two similarity measures have been compared: the cosine distance and the dice coefficient. Research has shown that the best results have been obtained using the cosine similarity distance and the multinomial naive Bayes classifier. The proposed approach combines the results of these two methods. Research on different cases of the proposed approach indicated the peculiarities of its application. At the same time, the combined approach allowed us to obtain a statistically significant increase in global accuracy.

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Filho, Geraldo P. Rocha, Li Weigang, Andre R. A. S. Santos, Ricardo Maia, and Liriam Enamoto. "Multi-label legal text classification with BiLSTM and attention." International Journal of Computer Applications in Technology 68, no. 4 (2022): 369. http://dx.doi.org/10.1504/ijcat.2022.10050321.

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Enamoto, Liriam, Andre R. A. S. Santos, Ricardo Maia, Li Weigang, and Geraldo P. Rocha Filho. "Multi-label legal text classification with BiLSTM and attention." International Journal of Computer Applications in Technology 68, no. 4 (2022): 369. http://dx.doi.org/10.1504/ijcat.2022.125186.

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Wang, Ran, Robert Ridley, Xi’ao Su, Weiguang Qu, and Xinyu Dai. "A novel reasoning mechanism for multi-label text classification." Information Processing & Management 58, no. 2 (2021): 102441. http://dx.doi.org/10.1016/j.ipm.2020.102441.

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Burkhardt, Sophie, and Stefan Kramer. "Online multi-label dependency topic models for text classification." Machine Learning 107, no. 5 (2017): 859–86. http://dx.doi.org/10.1007/s10994-017-5689-6.

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Verma, Gurudatta, and Tirath Prasad Sahu. "Deep label relevance and label ambiguity based multi-label feature selection for text classification." Engineering Applications of Artificial Intelligence 148 (May 2025): 110403. https://doi.org/10.1016/j.engappai.2025.110403.

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Abdillah, Abid Famasya, Cornelius Bagus Purnama Putra, Apriantoni Apriantoni, Safitri Juanita, and Diana Purwitasari. "Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data." Journal of Information Systems Engineering and Business Intelligence 8, no. 1 (2022): 42–50. http://dx.doi.org/10.20473/jisebi.8.1.42-50.

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Background: Question-answer (QA) is a popular method to seek health-related information and biomedical data. Such questions can refer to more than one medical entity (multi-label) so determining the correct tags is not easy. The question classification (QC) mechanism in a QA system can narrow down the answers we are seeking. Objective: This study develops a multi-label classification using the heterogeneous ensembles method to improve accuracy in biomedical data with long text dimensions. Methods: We used the ensemble method with heterogeneous deep learning and machine learning for multi-label extended text classification. There are 15 various single models consisting of three deep learning (CNN, LSTM, and BERT) and four machine learning algorithms (SVM, kNN, Decision Tree, and Naïve Bayes) with various text representations (TF-IDF, Word2Vec, and FastText). We used the bagging approach with a hard voting mechanism for the decision-making. Results: The result shows that deep learning is more powerful than machine learning as a single multi-label biomedical data classification method. Moreover, we found that top-three was the best number of base learners by combining the ensembles method. Heterogeneous-based ensembles with three learners resulted in an F1-score of 82.3%, which is better than the best single model by CNN with an F1-score of 80%. Conclusion: A multi-label classification of biomedical QA using ensemble models is better than single models. The result shows that heterogeneous ensembles are more potent than homogeneous ensembles on biomedical QA data with long text dimensions. Keywords: Biomedical Question Classification, Ensemble Method, Heterogeneous Ensembles, Multi-Label Classification, Question Answering

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Meng, Chunyun, Yuki Todo, Cheng Tang, Li Luan, and Zheng Tang. "MFLSCI: Multi-granularity fusion and label semantic correlation information for multi-label legal text classification." Engineering Applications of Artificial Intelligence 139 (January 2025): 109604. http://dx.doi.org/10.1016/j.engappai.2024.109604.

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Cai, Huali, Xuanya Shao, Pengpeng Zhou, and Hongtao Li. "Multi-Label Classification of Complaint Texts: Civil Aviation Service Quality Case Study." Electronics 14, no. 3 (2025): 434. https://doi.org/10.3390/electronics14030434.

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Customer complaints play an important role in the adjustment of business operations and improvement of services, particularly in the aviation industry. However, extracting adequate textual features to perform a multi-label classification of complaints remains a difficult problem. Current multi-label classification methods applied to complaint texts have not been able to fully utilize complaint information, and little research has been performed on complaint classification in the aviation industry. Therefore, to solve the problems of insufficient text feature extraction and the insufficient learning of inter-feature relationships, we constructed a multi-label classification model (MAG, or multi-feature attention gradient boosting decision tree classifier) for civil aviation service quality complaint texts. This model incorporates multiple features and attention mechanisms to improve the classification accuracy. First, the BERT (Bidirectional Encoder Representations from Transformers) model and attention mechanisms are used to represent the semantic and label features of the text. Then, the Text-CNN (a convolutional neural network) and BiLSTM (bidirectional long short-term memory) multi-channel feature extraction networks are used to extract the local and global features of the complaint text, respectively. Subsequently, a co-attention mechanism is used to learn the relationship between the local and global features. Finally, the travelers’ complaint texts are accurately classified by integrating the base classifiers. The results show that our proposed model improves the multi-label classification accuracy, outperforming other modern algorithms. We demonstrate how the label feature representation based on association rules and the multi-channel feature extraction network can enrich textual information and more fully extract features. Overall, the co-attention mechanism can effectively learn the relationships between text features, thereby improving the classification accuracy of the model and enabling better identification of travelers’ complaints. This study not only effectively extracted text features by integrating multiple features and attention mechanisms, but also constructed a targeted feature word set for complaint texts based on the domain-specific characteristics of the civil aviation industry. Furthermore, by iterating the basic classifier using a multi-label classification model, a classifier with higher accuracy was successfully obtained, providing strong technical support and new practical paths for improving the civil aviation service quality and complaint management.

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Liu, Wenbin, Bojian Wen, Shang Gao, Jiesheng Zheng, and Yinlong Zheng. "A multi-label text classification model based on ELMo and attention." MATEC Web of Conferences 309 (2020): 03015. http://dx.doi.org/10.1051/matecconf/202030903015.

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Text classification is a common application in natural language processing. We proposed a multi-label text classification model based on ELMo and attention mechanism which help solve the problem for the sentiment classification task that there is no grammar or writing convention in power supply related text and the sentiment related information disperses in the text. Firstly, we use pre-trained word embedding vector to extract the feature of text from the Internet. Secondly, the analyzed deep information features are weighted according to the attention mechanism. Finally, an improved ELMo model in which we replace the LSTM module with GRU module is used to characterize the text and information is classified. The experimental results on Kaggle’s toxic comment classification data set show that the accuracy of sentiment classification is as high as 98%.

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Peng, Liwen, and Yongguo Liu. "Gravitation Theory Based Model for Multi-Label Classification." International Journal of Computers Communications & Control 12, no. 5 (2017): 689. http://dx.doi.org/10.15837/ijccc.2017.5.2926.

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The past decade has witnessed the growing popularity in multi-label classification algorithms in the fields like text categorization, music information retrieval, and the classification of videos and medical proteins. In the meantime, the methods based on the principle of universal gravitation have been extensively used in the classification of machine learning owing to simplicity and high performance. In light of the above, this paper proposes a novel multi-label classification algorithm called the interaction and data gravitation-based model for multi-label classification (ITDGM). The algorithm replaces the interaction between two objects with the attraction between two particles. The author carries out a series of experiments on five multi-label datasets. The experimental results show that the ITDGM performs better than some well-known multi-label classification algorithms. The effect of the proposed model is assessed by the example-based F1-Measure and Label-based micro F1-measure.

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Zhang, Yongjun, Zijian Wang, Yongtao Yu, Bolun Chen, Jialin Ma, and Liang Shi. "LF-LDA." International Journal of Data Warehousing and Mining 14, no. 2 (2018): 18–36. http://dx.doi.org/10.4018/ijdwm.2018040102.

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This article describes how text documents are a major data structure in the era of big data. With the explosive growth of data, the number of documents with multi-labels has increased dramatically. The popular multi-label classification technology, which is usually employed to handle multinomial text documents, is sensitive to the noise terms of text documents. Therefore, there still exists a huge room for multi-label classification of text documents. This article introduces a supervised topic model, named labeled LDA with function terms (LF-LDA), to filter out the noisy function terms from text documents, which can help to improve the performance of multi-label classification of text documents. The article also shows the derivation of the Gibbs Sampling formulas in detail, which can be generalized to other similar topic models. Based on the textual data set RCV1-v2, the article compared the proposed model with other two state-of-the-art multi-label classifiers, Tuned SVM and labeled LDA, on both Macro-F1 and Micro-F1 metrics. The result shows that LF-LDA outperforms them and has the lowest variance, which indicates the robustness of the LF-LDA classifier.

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Pereira, Rafael B., Alexandre Plastino, Bianca Zadrozny, and Luiz H. C. Merschmann. "A lazy feature selection method for multi-label classification." Intelligent Data Analysis 25, no. 1 (2021): 21–34. http://dx.doi.org/10.3233/ida-194878.

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In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.

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Manoharan J, Samuel. "Capsule Network Algorithm for Performance Optimization of Text Classification." March 2021 3, no. 1 (2021): 1–9. http://dx.doi.org/10.36548/jscp.2021.1.001.

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In regions of visual inference, optimized performance is demonstrated by capsule networks on structured data. Classification of hierarchical multi-label text is performed with a simple capsule network algorithm in this paper. It is further compared to support vector machine (SVM), Long Short Term Memory (LSTM), artificial neural network (ANN), convolutional Neural Network (CNN) and other neural and non-neural network architectures to demonstrate its superior performance. The Blurb Genre Collection (BGC) and Web of Science (WOS) datasets are used for experimental purpose. The encoded latent data is combined with the algorithm while handling structurally diverse categories and rare events in hierarchical multi-label text applications.

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Prasanna, N. Lakshmi, R. Vaishnavi, V. Prasanna Lakshmi, V. Dakshayani, and T. Keerthana. "MULTI LABEL CLASSIFICATION FOR AN IMAGE USING CONVOLUTIONAL NEURAL NETWORKS." International Journal of Computer Science and Mobile Computing 10, no. 7 (2021): 1–9. http://dx.doi.org/10.47760/ijcsmc.2021.v10i07.001.

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The machine learning has many capabilities one of them is classification. Classification employed in many contexts like telling hotel reviews good / bad, or inferring the image consists of dog, cat etc. As the data increases there is a need to organize it, for that purpose classification can be helpful. Modern classification problems involve the prediction of multiple labels simultaneously associated with a single instance known as "Multi Label Classification". In multi-label classification, each of the input data samples belongs to one or more than one classes or labels. The traditional binary and multi-class classification problems are the subset of the multi-label classification problem. In this paper we are implementing the multi label classification using CNN framework with keras libraries. Classification can be applied to different domain such as text, audio etc. In this paper we are applying classification for an image dataset.

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Zhang, Ke, Yufei Tu, Jun Lu, et al. "Multi-Head Hierarchical Attention Framework with Multi-Level Learning Optimization Strategy for Legal Text Recognition." Electronics 14, no. 10 (2025): 1946. https://doi.org/10.3390/electronics14101946.

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Owing to the rapid increase in the amount of legal text data and the increasing demand for intelligent processing, multi-label legal text recognition is becoming increasingly important in practical applications such as legal information retrieval and case classification. However, traditional methods have limitations in handling the complex semantics and multi-label characteristics of legal texts, making it difficult to accurately extract feature and effective category information. Therefore, this study proposes a novel multi-head hierarchical attention framework suitable for multi-label legal text recognition tasks. This framework comprises a feature extraction module and a hierarchical module. The former extracts multi-level semantic representations of text, while the latter obtains multi-label category information. In addition, this study proposes a novel hierarchical learning optimization strategy that balances the learning needs of multi-level semantic representation and multi-label category information through data preprocessing, loss calculation, and weight updating, effectively accelerating the convergence speed of framework training. We conducted comparative experiments on the legal domain dataset CAIL2021 and the general multi-label recognition datasets AAPD and Web of Science (WOS). The results indicate that the method proposed in this study is significantly superior to mainstream methods in legal and general scenarios, demonstrating excellent performance. The study findings are expected to be widely applied in the field of intelligent processing of legal information, improving the accuracy of intelligent classification of judicial cases and further promoting the digitalization and intelligence process of the legal industry.

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Karsana, M. Mahfi Nurandi, Kemas Muslim L., and Widi Astuti. "Single-Label and Multi-Label Text Classification using ANN and Comparison with Naïve Bayes and SVM." JURNAL MEDIA INFORMATIKA BUDIDARMA 7, no. 2 (2023): 857. http://dx.doi.org/10.30865/mib.v7i2.6024.

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Machine learning has become useful in daily life thanks to improvements in machine learning techniques. Text classification as an important part in machine learning. There are already many methods used for text classification such as Artificial Neural Network (ANN), Naïve Bayes, SVM, Decision Tree etc. ANN is a branch in machine learning which approximate the function of natural neural network. ANN have been used extensively for classification. In this research a simple architecture of ANN is used. But it needs to be pointed out that the architecture used in this research is relatively simple compared to the cutting edge in ANN development and research to show the potential that ANN have compared to other classification method. ANN, Naïve Bayes and SVM performance are measured using f1-macro. Performance of classification model is measured of multiple single-label and multi-label dataset. This research found that in single-label classification ANN have a comparable f1-macro with 0.79 compared to 0.82 for SVM. In multi-label classification ANN have the best f1-macro with 0.48 compared to 0.44 in SVM.

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Zhang, Xinyi, Jiahao Xu, Charlie Soh, and Lihui Chen. "LA-HCN: Label-based Attention for Hierarchical Multi-label Text Classification Neural Network." Expert Systems with Applications 187 (January 2022): 115922. http://dx.doi.org/10.1016/j.eswa.2021.115922.

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Li, Anqi, and Lin Zhang. "Multi-Label Text Classification Based on Label-Sentence Bi-Attention Fusion Network with Multi-Level Feature Extraction." Electronics 14, no. 1 (2025): 185. https://doi.org/10.3390/electronics14010185.

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Multi-label text classification (MLTC) aims to assign the most appropriate label or labels to each input text. Previous studies have focused on mining textual information, ignoring the interdependence of labels and texts, thus leading to the loss of information about labels. In addition, previous studies have tended to focus on the single granularity of information in documents, ignoring the degree of inclination towards labels in different sentences in multi-labeled texts. In order to solve the above problems, this paper proposes a Label-Sentence Bi-Attention Fusion Network (LSBAFN) with multi-level feature extraction for mining multi-granularity information and label information in documents. Specifically, document-level and sentence-level word embeddings are first obtained. Then, the textual relevance of the labels to these two levels is utilized to construct sentence-level textual representations. Next, a multi-level feature extraction mechanism is utilized to acquire a sentence-level textual representation that incorporates contextual information and a document-level textual representation that reflects label features. Subsequently, the label-sentence bi-attention fusion mechanism is used to learn the feature relationships in the two text representations and fuse them. Label attention identifies text features related to labels from the document-level text representation, while sentence attention focuses on the tendency of sentences towards labels. Finally, the effective portion of the fused features is extracted for classification by a multi-layer perceptron. The experimental findings indicate that the LSBAFN can improve the effectiveness of the MLTC task. Compared with the baseline models, the LSBAFN obtains a significant improvement of 0.6% and 7.81% in Micro-F1 and Macro-F1 on the Article Topic dataset and improvements of 1.03% and 0.47% in P@k and 1.02% and 0.38% in nDCG@k on the Software Category dataset and RCV1 dataset.

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Zhao, Meijie, and Guozhu Liu. "Research on Multi-Label Legal Text Categorization Methods Based on Large Predicate Models." Journal of Combinatorial Mathematics and Combinatorial Computing 127a (April 15, 2025): 2349–69. https://doi.org/10.61091/jcmcc127a-134.

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In this paper, the embedding vectors are obtained by Bert coding, and then the obtained embedding vectors are adaptively fused with features to realize legal text classification by a classifier, on the basis of which a multi-label text classification model (AFDAM) is proposed to capture the target words in a sentence. At the same time, the pre-trained continuous bag-of-words representation (CBOW) is used to initialize the vector representation of the label information, and then these label information is adaptively fused with the feature information of the text, which effectively promotes the multi-label legal text classification, and accelerates the development of informationization and intelligence in the legal field. The results show that the text feature enhancement module has the most prominent impact on the text classification effect, and its accuracy on the three datasets is improved by 0.46%-1.19%. In addition, the introduction of target vectors and text expansion also gained 0.54%-1.7% and 0.59%-1.53% and 1.08% increases in model accuracy, respectively. In addition, the addition of offense and statute information can significantly improve the prediction of sentence length, and the statute information improves the results more significantly than the offense information. And the classification effect of the AFDAM model proposed in this paper increased by 0.1453-0.257 than the other five models.

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Wang, Boyan, Xuegang Hu, Peipei Li, and Philip S. Yu. "Cognitive structure learning model for hierarchical multi-label text classification." Knowledge-Based Systems 218 (April 2021): 106876. http://dx.doi.org/10.1016/j.knosys.2021.106876.

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Karpovich, Sergey Nikolaevich. "Multi-Label Classification of Text Documents using Probabilistic Topic Modeling." SPIIRAS Proceedings 4, no. 47 (2016): 92. http://dx.doi.org/10.15622/sp.47.5.

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Tu, Min, and Shiyang Xu. "Multi-label text classification algorithm based on semi-supervised learning." Journal of Physics: Conference Series 1629 (September 2020): 012067. http://dx.doi.org/10.1088/1742-6596/1629/1/012067.

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Gargiulo, Francesco, Stefano Silvestri, Mario Ciampi, and Giuseppe De Pietro. "Deep neural network for hierarchical extreme multi-label text classification." Applied Soft Computing 79 (June 2019): 125–38. http://dx.doi.org/10.1016/j.asoc.2019.03.041.

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Yang, Zhenyu, and Guojing Liu. "Hierarchical Sequence-to-Sequence Model for Multi-Label Text Classification." IEEE Access 7 (2019): 153012–20. http://dx.doi.org/10.1109/access.2019.2948855.

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