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Zeitschriftenartikel zum Thema „Hierarchical Multi-label Text Classification“

Autor: Grafiati

Veröffentlicht am 25. Juni 2021

Zuletzt aktualisiert am 16. Februar 2022

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1

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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2

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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3

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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4

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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5

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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Vogrincic, Sergeja, and Zoran Bosnic. "Ontology-based multi-label classification of economic articles." Computer Science and Information Systems 8, no. 1 (2011): 101–19. http://dx.doi.org/10.2298/csis100420034v.

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The paper presents an approach to the task of automatic document categorization in the field of economics. Since the documents can be annotated with multiple keywords (labels), we approach this task by applying and evaluating multi-label classification methods of supervised machine learning. We describe forming a test corpus of 1015 economic documents that we automatically classify using a tool which integrates ontology construction with text mining methods. In our experimental work, we evaluate three groups of multi-label classification approaches: transformation to single-class problems, specialized multi-label models, and hierarchical/ranking models. The classification accuracies of all tested classification models indicate that there is a potential for using all of the evaluated methods to solve this task. The results show the benefits of using complex groups of approaches which benefit from exploiting dependence between the labels. A good alternative to these approaches is also single-class naive Bayes classifiers coupled with the binary relevance transformation approach.

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Gong, Jibing, Hongyuan Ma, Zhiyong Teng, et al. "Hierarchical Graph Transformer-Based Deep Learning Model for Large-Scale Multi-Label Text Classification." IEEE Access 8 (2020): 30885–96. http://dx.doi.org/10.1109/access.2020.2972751.

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8

Sohrab, Mohammad Golam, Makoto Miwa, and Yutaka Sasaki. "IN-DEDUCTIVE and DAG-Tree Approaches for Large-Scale Extreme Multi-label Hierarchical Text Classification." Polibits 54 (July 31, 2016): 61–70. http://dx.doi.org/10.17562/pb-54-8.

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9

Deng, Jiawen, and Fuji Ren. "Hierarchical Network with Label Embedding for Contextual Emotion Recognition." Research 2021 (January 6, 2021): 1–9. http://dx.doi.org/10.34133/2021/3067943.

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Emotion recognition has been used widely in various applications such as mental health monitoring and emotional management. Usually, emotion recognition is regarded as a text classification task. Emotion recognition is a more complex problem, and the relations of emotions expressed in a text are nonnegligible. In this paper, a hierarchical model with label embedding is proposed for contextual emotion recognition. Especially, a hierarchical model is utilized to learn the emotional representation of a given sentence based on its contextual information. To give emotion correlation-based recognition, a label embedding matrix is trained by joint learning, which contributes to the final prediction. Comparison experiments are conducted on Chinese emotional corpus RenCECps, and the experimental results indicate that our approach has a satisfying performance in textual emotion recognition task.

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Liu, Zhenyu, Chaohong Lu, Haiwei Huang, Shengfei Lyu, and Zhenchao Tao. "Hierarchical Multi-Granularity Attention- Based Hybrid Neural Network for Text Classification." IEEE Access 8 (2020): 149362–71. http://dx.doi.org/10.1109/access.2020.3016727.

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11

Kadriu, Arbana, Lejla Abazi, and Hyrije Abazi. "Albanian Text Classification: Bag of Words Model and Word Analogies." Business Systems Research Journal 10, no. 1 (2019): 74–87. http://dx.doi.org/10.2478/bsrj-2019-0006.

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Abstract Background: Text classification is a very important task in information retrieval. Its objective is to classify new text documents in a set of predefined classes, using different supervised algorithms. Objectives: We focus on the text classification for Albanian news articles using two approaches. Methods/Approach: In the first approach, the words in a collection are considered as independent components, allocating to each of them a conforming vector in the vector’s space. Here we utilized nine classifiers from the scikit-learn package, training the classifiers with part of news articles (80%) and testing the accuracy with the remaining part of these articles. In the second approach, the text classification treats words based on their semantic and syntactic word similarities, supposing a word is formed by n-grams of characters. In this case, we have used the fastText, a hierarchical classifier, that considers local word order, as well as sub-word information. We have measured the accuracy for each classifier separately. We have also analyzed the training and testing time. Results: Our results show that the bag of words model does better than fastText when testing the classification process for not a large dataset of text. FastText shows better performance when classifying multi-label text. Conclusions: News articles can serve to create a benchmark for testing classification algorithms of Albanian texts. The best results are achieved with a bag of words model, with an accuracy of 94%.

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La Grassa, Riccardo, Ignazio Gallo, and Nicola Landro. "Learn class hierarchy using convolutional neural networks." Applied Intelligence 51, no. 10 (2021): 6622–32. http://dx.doi.org/10.1007/s10489-020-02103-6.

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AbstractA large amount of research on Convolutional Neural Networks (CNN) has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed as hierarchical classification problems, in which the classes to be predicted are organized in a hierarchy of classes. In this paper, we propose a new architecture for hierarchical classification, introducing a stack of deep linear layers using cross-entropy loss functions combined to a center loss function. The proposed architecture can extend any neural network model and simultaneously optimizes loss functions to discover local hierarchical class relationships and a loss function to discover global information from the whole class hierarchy while penalizing class hierarchy violations. We experimentally show that our hierarchical classifier presents advantages to the traditional classification approaches finding application in computer vision tasks. The same approach can also be applied to some CNN for text classification.

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Li, Wenkuan, Dongyuan Li, Hongxia Yin, Lindong Zhang, Zhenfang Zhu, and Peiyu Liu. "Lexicon-Enhanced Attention Network Based on Text Representation for Sentiment Classification." Applied Sciences 9, no. 18 (2019): 3717. http://dx.doi.org/10.3390/app9183717.

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Text representation learning is an important but challenging issue for various natural language processing tasks. Recently, deep learning-based representation models have achieved great success for sentiment classification. However, these existing models focus on more semantic information rather than sentiment linguistic knowledge, which provides rich sentiment information and plays a key role in sentiment analysis. In this paper, we propose a lexicon-enhanced attention network (LAN) based on text representation to improve the performance of sentiment classification. Specifically, we first propose a lexicon-enhanced attention mechanism by combining the sentiment lexicon with an attention mechanism to incorporate sentiment linguistic knowledge into deep learning methods. Second, we introduce a multi-head attention mechanism in the deep neural network to interactively capture the contextual information from different representation subspaces at different positions. Furthermore, we stack a LAN model to build a hierarchical sentiment classification model for large-scale text. Extensive experiments are conducted to evaluate the effectiveness of the proposed models on four popular real-world sentiment classification datasets at both the sentence level and the document level. The experimental results demonstrate that our proposed models can achieve comparable or better performance than the state-of-the-art methods.

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Liu, Tianyu, Fuli Luo, Qiaolin Xia, Shuming Ma, Baobao Chang, and Zhifang Sui. "Hierarchical Encoder with Auxiliary Supervision for Neural Table-to-Text Generation: Learning Better Representation for Tables." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 6786–93. http://dx.doi.org/10.1609/aaai.v33i01.33016786.

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Generating natural language descriptions for the structured tables which consist of multiple attribute-value tuples is a convenient way to help people to understand the tables. Most neural table-to-text models are based on the encoder-decoder framework. However, it is hard for a vanilla encoder to learn the accurate semantic representation of a complex table. The challenges are two-fold: firstly, the table-to-text datasets often contain large number of attributes across different domains, thus it is hard for the encoder to incorporate these heterogeneous resources. Secondly, the single encoder also has difficulties in modeling the complex attribute-value structure of the tables. To this end, we first propose a two-level hierarchical encoder with coarse-to-fine attention to handle the attribute-value structure of the tables. Furthermore, to capture the accurate semantic representations of the tables, we propose 3 joint tasks apart from the prime encoder-decoder learning, namely auxiliary sequence labeling task, text autoencoder and multi-labeling classification, as the auxiliary supervisions for the table encoder. We test our models on the widely used dataset WIKIBIO which contains Wikipedia infoboxes and related descriptions. The dataset contains complex tables as well as large number of attributes across different domains. We achieve the state-of-the-art performance on both automatic and human evaluation metrics.

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Mukhamediev, Ravil I., Kirill Yakunin, Rustam Mussabayev, et al. "Classification of Negative Information on Socially Significant Topics in Mass Media." Symmetry 12, no. 12 (2020): 1945. http://dx.doi.org/10.3390/sym12121945.

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Mass media not only reflect the activities of state bodies but also shape the informational context, sentiment, depth, and significance level attributed to certain state initiatives and social events. Multilateral and quantitative (to the practicable extent) assessment of media activity is important for understanding their objectivity, role, focus, and, ultimately, the quality of the society’s “fourth power”. The paper proposes a method for evaluating the media in several modalities (topics, evaluation criteria/properties, classes), combining topic modeling of the text corpora and multiple-criteria decision making. The evaluation is based on an analysis of the corpora as follows: the conditional probability distribution of media by topics, properties, and classes is calculated after the formation of the topic model of the corpora. Several approaches are used to obtain weights that describe how each topic relates to each evaluation criterion/property and to each class described in the paper, including manual high-level labeling, a multi-corpora approach, and an automatic approach. The proposed multi-corpora approach suggests assessment of corpora topical asymmetry to obtain the weights describing each topic’s relationship to a certain criterion/property. These weights, combined with the topic model, can be applied to evaluate each document in the corpora according to each of the considered criteria and classes. The proposed method was applied to a corpus of 804,829 news publications from 40 Kazakhstani sources published from 01 January 2018 to 31 December 2019, to classify negative information on socially significant topics. A BigARTM model was derived (200 topics) and the proposed model was applied, including to fill a table of the analytical hierarchical process (AHP) and all of the necessary high-level labeling procedures. Experiments confirm the general possibility of evaluating the media using the topic model of the text corpora, because an area under receiver operating characteristics curve (ROC AUC) score of 0.81 was achieved in the classification task, which is comparable with results obtained for the same task by applying the BERT (Bidirectional Encoder Representations from Transformers) model.

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CHEN, KE, DAHONG XIE, and HUISHENG CHI. "SPEAKER IDENTIFICATION USING TIME-DELAY HMEs." International Journal of Neural Systems 07, no. 01 (1996): 29–43. http://dx.doi.org/10.1142/s012906579600004x.

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In this paper, we extend the Hierarchical Mixture of Experts (HME) to temporal processing and explore it for a substantial problem, that of text-dependent speaker identification. For a specific multiway classification, we propose a generalized Bernoulli density instead of the multinomial logit density to avoid the instability during training. Time-delay technique is applied for spatio-temporal processing in the HME and a combining scheme is presented for combining multiple time-delay HMEs in order to complete a multi-scale analysis for the temporal data. Using the time-delay HME along with the EM algorithm as well as the combination of multiple time-delay HMEs, the speaker identification system has a good performance and yields significantly fast training. We have also addressed some issues about the time-delay techniques in the HME.

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Ghazal, Rubina, Ahmad Malik, Basit Raza, Nauman Qadeer, Nafees Qamar, and Sajal Bhatia. "Agent-Based Semantic Role Mining for Intelligent Access Control in Multi-Domain Collaborative Applications of Smart Cities." Sensors 21, no. 13 (2021): 4253. http://dx.doi.org/10.3390/s21134253.

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Significance and popularity of Role-Based Access Control (RBAC) is inevitable; however, its application is highly challenging in multi-domain collaborative smart city environments. The reason is its limitations in adapting the dynamically changing information of users, tasks, access policies and resources in such applications. It also does not incorporate semantically meaningful business roles, which could have a diverse impact upon access decisions in such multi-domain collaborative business environments. We propose an Intelligent Role-based Access Control (I-RBAC) model that uses intelligent software agents for achieving intelligent access control in such highly dynamic multi-domain environments. The novelty of this model lies in using a core I-RBAC ontology that is developed using real-world semantic business roles as occupational roles provided by Standard Occupational Classification (SOC), USA. It contains around 1400 business roles, from nearly all domains, along with their detailed task descriptions as well as hierarchical relationships among them. The semantic role mining process is performed through intelligent agents that use word embedding and a bidirectional LSTM deep neural network for automated population of organizational ontology from its unstructured text policy and, subsequently, matching this ontology with core I-RBAC ontology to extract unified business roles. The experimentation was performed on a large number of collaboration case scenarios of five multi-domain organizations and promising results were obtained regarding the accuracy of automatically derived RDF triples (Subject, Predicate, Object) from organizational text policies as well as the accuracy of extracted semantically meaningful roles.

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Esuli, Andrea, Tiziano Fagni, and Fabrizio Sebastiani. "Boosting multi-label hierarchical text categorization." Information Retrieval 11, no. 4 (2008): 287–313. http://dx.doi.org/10.1007/s10791-008-9047-y.

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Kumar, Vikas, Arun K. Pujari, Vineet Padmanabhan, Sandeep Kumar Sahu, and Venkateswara Rao Kagita. "Multi-label classification using hierarchical embedding." Expert Systems with Applications 91 (January 2018): 263–69. http://dx.doi.org/10.1016/j.eswa.2017.09.020.

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20

Kasi, Anup, Weijing Sun, Sumia Ehsan, et al. "Early detection of pancreatic cancers by novel nanobiosensor-based protease biomarkers using hierarchical decision structure." Journal of Clinical Oncology 39, no. 15_suppl (2021): e16273-e16273. http://dx.doi.org/10.1200/jco.2021.39.15_suppl.e16273.

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e16273 Background: There is a critical need to develop fast, reliable, and cost-effective methods for the detection of pancreatic cancer (PC) at the earliest stage to maximize the impact of treatment. To-date, early detection of PC is close to impossible due to the location of the pancreas and the absence of characteristic symptoms in early cancer stages. Methods: Our team of clinicians and scientists has established a fast and reliable nanobiosensor technology that comprises iron/iron oxide nanoparticles attached to a protease or arginase activatable FRET pair (tetrakis (4- carboxyphenyl) porphyrin (TCPP) /cyanine 5.5). Arginase and seven proteases (MMP1, 3, and 9, cathepsin B, and E, urokinase plasminogen activator, and neutrophil elastase) were identified using the Gene Expression Omnibus (GEO) web tool based on their different expression pattern in pancreatic cancer patients, pancreatitis and healthy control subjects. Protease/arginase activities were measured in serum after 1h of incubation. Based on this data, a novel engineering approach to improved early stage detection of pancreatic cancer is reported here. This study was funded by American Cancer Society Institutional Research Grant (IRG‐16‐194‐07), awarded to the University of Kansas Medical Center. Results: In our study, 159 patients were enrolled at KU Cancer Center from 2000-2019, 47 with metastatic PC, 36 with localized PC, 26 pancreatitis and 50 healthy controls using KUCC Biospecimen Repository. The problem of early stage detection of pancreatic cancer can be modeled as a multi-class classification problem. Conventional classification approaches provide at most 77% accuracy for the dataset under consideration. A new hierarchical decision structure with specific feature engineering at each step is introduced here to improve the performance of the classifier. The fundamental premise of this information fusion-based framework involves tailoring the statistically most significant features with appropriate weights to execute an efficient binary classification task at each hierarchical step. An overall accuracy of 95% was achieved for the detection of patients with early pancreatic cancer (see table). Conclusions: Because of the dire survival statistics of pancreatic cancer, detection at the earliest possible time by means of a liquid biopsy will offer the greatest benefit. Novel nanobiosensor based protease biomarkers achieved high accuracy in early detection of pancreatic cancers by applying hierarchical decision structure. Our results need validation in a larger cohort. Predicted true class considering the following combination of classification methods: Step1 – kNN*, step2 – kNN*, step3 – RFC* (Accuracy = 94.97%).[Table: see text]

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Vens, Celine, Jan Struyf, Leander Schietgat, Sašo Džeroski, and Hendrik Blockeel. "Decision trees for hierarchical multi-label classification." Machine Learning 73, no. 2 (2008): 185–214. http://dx.doi.org/10.1007/s10994-008-5077-3.

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Nakano, Felipe Kenji, Ricardo Cerri, and Celine Vens. "Active learning for hierarchical multi-label classification." Data Mining and Knowledge Discovery 34, no. 5 (2020): 1496–530. http://dx.doi.org/10.1007/s10618-020-00704-w.

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Murawaki, Yugo. "Exploiting Inter-label Dependencies in Hierarchical Multi-Label Document Classification." Journal of Natural Language Processing 21, no. 1 (2014): 41–60. http://dx.doi.org/10.5715/jnlp.21.41.

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Chen, Boli, Xin Huang, Lin Xiao, Zixin Cai, and Liping Jing. "Hyperbolic Interaction Model for Hierarchical Multi-Label Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 7496–503. http://dx.doi.org/10.1609/aaai.v34i05.6247.

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Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every instance with the labels organized under hierarchical relations. Besides the labels, since linguistic ontologies are intrinsic hierarchies, the conceptual relations between words can also form hierarchical structures. Thus it can be a challenge to learn mappings from word hierarchies to label hierarchies. We propose to model the word and label hierarchies by embedding them jointly in the hyperbolic space. The main reason is that the tree-likeness of the hyperbolic space matches the complexity of symbolic data with hierarchical structures. A new Hyperbolic Interaction Model (HyperIM) is designed to learn the label-aware document representations and make predictions for HMLC. Extensive experiments are conducted on three benchmark datasets. The results have demonstrated that the new model can realistically capture the complex data structures and further improve the performance for HMLC comparing with the state-of-the-art methods. To facilitate future research, our code is publicly available.

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Triguero, Isaac, and Celine Vens. "Labelling strategies for hierarchical multi-label classification techniques." Pattern Recognition 56 (August 2016): 170–83. http://dx.doi.org/10.1016/j.patcog.2016.02.017.

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Cerri, Ricardo, Rodrigo C. Barros, and André C. P. L. F. de Carvalho. "Hierarchical multi-label classification using local neural networks." Journal of Computer and System Sciences 80, no. 1 (2014): 39–56. http://dx.doi.org/10.1016/j.jcss.2013.03.007.

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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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Levatić, Jurica, Dragi Kocev, and Sašo Džeroski. "The importance of the label hierarchy in hierarchical multi-label classification." Journal of Intelligent Information Systems 45, no. 2 (2014): 247–71. http://dx.doi.org/10.1007/s10844-014-0347-y.

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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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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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Zhang, L., S. K. Shah, and I. A. Kakadiaris. "Hierarchical Multi-label Classification using Fully Associative Ensemble Learning." Pattern Recognition 70 (October 2017): 89–103. http://dx.doi.org/10.1016/j.patcog.2017.05.007.

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Slavkov, Ivica, Jana Karcheska, Dragi Kocev, and Saso Dzeroski. "HMC-ReliefF: Feature ranking for hierarchical multi-label classification." Computer Science and Information Systems 15, no. 1 (2018): 187–209. http://dx.doi.org/10.2298/csis170115043s.

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In machine learning, the growing complexity of the available data poses an increased challenge for its analysis. The rising complexity is both in terms of the data becoming more high-dimensional as well as the data having a more intricate structure. This emphasizes the need for developing machine learning algorithms that are able to tackle both the high-dimensionality and the complex structure of the data. Our work in this paper focuses on the development and analysis of the HMCReliefF algorithm, which is a feature relevance (ranking) algorithm for the task of Hierarchical Multi-label Classification (HMC). The basis of the algorithm is the RReliefF algorithm for regression that is adapted for hierarchical multi-label target variables. We perform an extensive experimental investigation of the HMC-ReliefF algorithm on several datasets from the domains of image annotation and functional genomics. We analyse the algorithm?s performance in terms of accuracy in a filterlike setting and also in terms of ranking stability for various parameter values. The results show that the HMC-ReliefF can successfully detect relevant features from the data that can be further used for constructing accurate predictive models. Additionally, the stability analysis helps to determine the preferred parameter values for obtaining not just accurate, but also a stable algorithm output.

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Zeng, Chunqiu, Wubai Zhou, Tao Li, Larisa Shwartz, and Genady Ya Grabarnik. "Knowledge Guided Hierarchical Multi-Label Classification Over Ticket Data." IEEE Transactions on Network and Service Management 14, no. 2 (2017): 246–60. http://dx.doi.org/10.1109/tnsm.2017.2668363.

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Daisey, Katie, and Steven D. Brown. "Effects of the hierarchy in hierarchical, multi-label classification." Chemometrics and Intelligent Laboratory Systems 207 (December 2020): 104177. http://dx.doi.org/10.1016/j.chemolab.2020.104177.

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Santos, Araken, and Anne Canuto. "Applying semi-supervised learning in hierarchical multi-label classification." Expert Systems with Applications 41, no. 14 (2014): 6075–85. http://dx.doi.org/10.1016/j.eswa.2014.03.052.

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Cerri, Ricardo, Márcio P. Basgalupp, Rodrigo C. Barros, and André C. P. L. F. de Carvalho. "Inducing Hierarchical Multi-label Classification rules with Genetic Algorithms." Applied Soft Computing 77 (April 2019): 584–604. http://dx.doi.org/10.1016/j.asoc.2019.01.017.

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Khan, Salabat, and Abdul Rauf Baig. "Ant colony optimization based hierarchical multi-label classification algorithm." Applied Soft Computing 55 (June 2017): 462–79. http://dx.doi.org/10.1016/j.asoc.2017.02.021.

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Stenger, B., A. Thayananthan, P. H. S. Torr, and R. Cipolla. "Estimating 3D hand pose using hierarchical multi-label classification." Image and Vision Computing 25, no. 12 (2007): 1885–94. http://dx.doi.org/10.1016/j.imavis.2005.12.018.

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40

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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41

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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42

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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43

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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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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Feng, Shou, Chunhui Zhao, and Ping Fu. "A deep neural network based hierarchical multi-label classification method." Review of Scientific Instruments 91, no. 2 (2020): 024103. http://dx.doi.org/10.1063/1.5141161.

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45

Kolisnik, Brendan, Isaac Hogan, and Farhana Zulkernine. "Condition-CNN: A hierarchical multi-label fashion image classification model." Expert Systems with Applications 182 (November 2021): 115195. http://dx.doi.org/10.1016/j.eswa.2021.115195.

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46

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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47

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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48

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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49

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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50

Irsan, Ivana Clairine, and Masayu Leylia Khodra. "Hierarchical multi-label news article classification with distributed semantic model based features." International Journal of Advances in Intelligent Informatics 5, no. 1 (2019): 40. http://dx.doi.org/10.26555/ijain.v5i1.168.

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Automatic news categorization is essential to automatically handle the classification of multi-label news articles in online portal. This research employs some potential methods to improve performance of hierarchical multi-label classifier for Indonesian news article. First potential method is using Convolutional Neural Network (CNN) to build the top level classifier. The second method could improve the classification performance by calculating the average of the word vectors obtained from distributed semantic model. The third method combines lexical and semantic method to extract documents features, which multiplied word term frequency (lexical) with word vector average (semantic). Model build using Calibrated Label Ranking as multi-label classification method, and trained using Naïve Bayes algorithm has the best F1-measure of 0.7531. Multiplication of word term frequency and the average of word vectors were also used to build this classifiers. This configuration improved multi-label classification performance by 4.25%, compared to the baseline. The distributed semantic model that gave best performance in this experiment obtained from 300-dimension word2vec of Wikipedia’s articles. The multi-label classification model performance is also influenced by news’ released date. The difference period between training and testing data would also decrease models’ performance.

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