Relevant bibliographies by topics / Classification multilabels

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Academic literature on the topic 'Classification multilabels'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Classification multilabels"

1

Lee, Jaesung, Wangduk Seo, and Dae-Won Kim. "Effective Evolutionary Multilabel Feature Selection under a Budget Constraint." Complexity 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/3241489.

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Multilabel feature selection involves the selection of relevant features from multilabeled datasets, resulting in improved multilabel learning accuracy. Evolutionary search-based multilabel feature selection methods have proved useful for identifying a compact feature subset by successfully improving the accuracy of multilabel classification. However, conventional methods frequently violate budget constraints or result in inefficient searches due to ineffective exploration of important features. In this paper, we present an effective evolutionary search-based feature selection method for multi
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Cerri, Ricardo, André Carlos P. L. F. de Carvalho, and Alex A. Freitas. "Adapting non-hierarchical multilabel classification methods for hierarchical multilabel classification." Intelligent Data Analysis 15, no. 6 (2011): 861–87. http://dx.doi.org/10.3233/ida-2011-0500.

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Sun, Kai Wei, Chong Ho Lee, and Jin Wang. "Multilabel Classification via Co-Evolutionary Multilabel Hypernetwork." IEEE Transactions on Knowledge and Data Engineering 28, no. 9 (2016): 2438–51. http://dx.doi.org/10.1109/tkde.2016.2566621.

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Li, Yong-Zhi, Guo-Zheng Li, Jian-Yi Gao, et al. "Syndrome Differentiation Analysis on Mars500 Data of Traditional Chinese Medicine." Scientific World Journal 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/125736.

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Mars500 study was a psychological and physiological isolation experiment conducted by Russia, the European Space Agency, and China, in preparation for an unspecified future manned spaceflight to the planet Mars. Its intention was to yield valuable psychological and medical data on the effects of the planned long-term deep space mission. In this paper, we present data mining methods to mine medical data collected from the crew consisting of six spaceman volunteers. The synthesis of the four diagnostic methods of TCM, inspection, listening, inquiry, and palpation, is used in our syndrome differe
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Zhao, Xiaowei, Zhigang Ma, Zhi Li, and Zhihui Li. "Joint Concept Correlation and Feature-Concept Relevance Learning for Multilabel Classification." Neural Computation 30, no. 2 (2018): 526–45. http://dx.doi.org/10.1162/neco_a_01036.

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In recent years, multilabel classification has attracted significant attention in multimedia annotation. However, most of the multilabel classification methods focus only on the inherent correlations existing among multiple labels and concepts and ignore the relevance between features and the target concepts. To obtain more robust multilabel classification results, we propose a new multilabel classification method aiming to capture the correlations among multiple concepts by leveraging hypergraph that is proved to be beneficial for relational learning. Moreover, we consider mining feature-conc
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Tewari, Abha, Pratik Sawant, Jai Samtani, Sanket Sawant, and Gaurav Massand. "Multilabel Classification of Tweets." International Journal of Computer Applications 159, no. 1 (2017): 1–4. http://dx.doi.org/10.5120/ijca2017912209.

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Kumari, H. M. N. S., and U. M. M. P. K. Nawarathne. "Machine Failure Prediction Using Multilabel Classification Methods." Journal of Advances in Engineering and Technology 2, no. 2 (2024): 37–45. http://dx.doi.org/10.54389/oknw9621.

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Early detection of machine failure is crucial in every industrial setting as it may prevent unexpected process downtimes as well as system failures. However, machine learning (ML) models are increasingly being utilized to forecast system failures in industrial maintenance, and among them, multilabel classification techniques act as efficient methods. Therefore, this study analyzed machine failure data with five types of machine failures. Initially, a feature selection approach was also carried out in this study to determine the variables which directly cause machine failure. Furthermore, multi
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Wang, Yichen, Yi Wang, and Zhimin Zhang. "Complexity Graph-Based Multilabel Classification Method of Human Action in Rope Skipping Scene." Security and Communication Networks 2022 (May 16, 2022): 1–7. http://dx.doi.org/10.1155/2022/8202383.

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Aiming at the problem of insufficient accuracy of multilabel classification of human action at present, a multilabel classification method of human action in the rope skipping scene is proposed. It realizes feature recognition and classification by collecting human action features in the scene of skipping rope movement and uses RNN to optimize the human action feature recognition algorithm. On the basis of feature recognition, the characteristics of human movement in the rope skipping scene are classified, the confidence map of the key point position is obtained by using the Gaussian modeling
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Peng, Liwen, and Yongguo Liu. "Feature Selection and Overlapping Clustering-Based Multilabel Classification Model." Mathematical Problems in Engineering 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/2814897.

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Multilabel classification (MLC) learning, which is widely applied in real-world applications, is a very important problem in machine learning. Some studies show that a clustering-based MLC framework performs effectively compared to a nonclustering framework. In this paper, we explore the clustering-based MLC problem. Multilabel feature selection also plays an important role in classification learning because many redundant and irrelevant features can degrade performance and a good feature selection algorithm can reduce computational complexity and improve classification accuracy. In this study
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Bi, Wei, and Jame T. Kwok. "Bayes-Optimal Hierarchical Multilabel Classification." IEEE Transactions on Knowledge and Data Engineering 27, no. 11 (2015): 2907–18. http://dx.doi.org/10.1109/tkde.2015.2441707.

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Dissertations / Theses on the topic "Classification multilabels"

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Lindig, León Cecilia. "Classification multilabels à partir de signaux EEG d'imaginations motrices combinées : application au contrôle 3D d'un bras robotique." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0016/document.

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Les interfaces cerveau-ordinateur (ou BCI en anglais pour Brain-Computer Interfaces) mettent en place depuis le système nerveux central un circuit artificiel secondaire qui remplace l’utilisation des nerfs périphériques, permettant entre autres à des personnes ayant une déficience motrice grave d’interagir, uniquement à l’aide de leur activité cérébrale, avec différents types d’applications, tels qu’un système d’écriture, une neuro-prothèse, un fauteuil roulant motorisé ou un bras robotique. Une technique répandue au sein des BCI pour enregistrer l’activité cérébrale est l’électroencéphalograp
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Lindig, León Cecilia. "Classification multilabels à partir de signaux EEG d'imaginations motrices combinées : application au contrôle 3D d'un bras robotique." Electronic Thesis or Diss., Université de Lorraine, 2017. http://www.theses.fr/2017LORR0016.

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Les interfaces cerveau-ordinateur (ou BCI en anglais pour Brain-Computer Interfaces) mettent en place depuis le système nerveux central un circuit artificiel secondaire qui remplace l’utilisation des nerfs périphériques, permettant entre autres à des personnes ayant une déficience motrice grave d’interagir, uniquement à l’aide de leur activité cérébrale, avec différents types d’applications, tels qu’un système d’écriture, une neuro-prothèse, un fauteuil roulant motorisé ou un bras robotique. Une technique répandue au sein des BCI pour enregistrer l’activité cérébrale est l’électroencéphalograp
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Cai, Lijuan. "Multilabel classification over category taxonomies." View abstract/electronic edition; access limited to Brown University users, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3318298.

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Cisse, Mouhamadou Moustapha. "Efficient extreme classification." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066594/document.

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Dans cette thèse, nous proposons des méthodes a faible complexité pour la classification en présence d'un très grand nombre de catégories. Ces methodes permettent d'accelerer la prediction des classifieurs afin des les rendre utilisables dans les applications courantes. Nous proposons deux methodes destinées respectivement a la classification monolabel et a la classification multilabel. La première méthode utilise l'information hierarchique existante entre les catégories afin de créer un représentation binaire compact de celles-ci. La seconde approche , destinée aux problemes multilabel adpate
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Cisse, Mouhamadou Moustapha. "Efficient extreme classification." Electronic Thesis or Diss., Paris 6, 2014. http://www.theses.fr/2014PA066594.

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Dans cette thèse, nous proposons des méthodes a faible complexité pour la classification en présence d'un très grand nombre de catégories. Ces methodes permettent d'accelerer la prediction des classifieurs afin des les rendre utilisables dans les applications courantes. Nous proposons deux methodes destinées respectivement a la classification monolabel et a la classification multilabel. La première méthode utilise l'information hierarchique existante entre les catégories afin de créer un représentation binaire compact de celles-ci. La seconde approche , destinée aux problemes multilabel adpate
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STEINBRUCH, DAVID. "A STUDY OF MULTILABEL TEXT CLASSIFICATION ALGORITHMS USING NAIVE-BAYES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9637@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO<br>A quantidade de informação eletrônica vem crescendo de forma acelerada, motivada principalmente pela facilidade de publicação e divulgação que a Internet proporciona. Desta forma, é necessária a organização da informação de forma a facilitar a sua aquisição. Muitos trabalhos propuseram resolver este problema através da classificação automática de textos associando a eles vários rótulos (classificação multirótulo). No entanto, estes trabalhos transformam este problema em subproblemas de classificação binária, considerando q
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Suta, Adin. "Multilabel text classification of public procurements using deep learning intent detection." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252558.

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Textual data is one of the most widespread forms of data and the amount of such data available in the world increases at a rapid rate. Text can be understood as either a sequence of characters or words, where the latter approach is the most common. With the breakthroughs within the area of applied artificial intelligence in recent years, more and more tasks are aided by automatic processing of text in various applications. The models introduced in the following sections rely on deep-learning sequence-processing in order to process and text to produce a regression algorithm for classification o
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Loza, Mencía Eneldo [Verfasser], Johannes [Akademischer Betreuer] Fürnkranz, and Hüllermeier [Akademischer Betreuer] Eyke. "Efficient Pairwise Multilabel Classification / Eneldo Loza Mencía. Betreuer: Johannes Fürnkranz ; Hüllermeier Eyke." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2013. http://d-nb.info/1107769655/34.

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Park, Sang-Hyeun [Verfasser], Johannes [Akademischer Betreuer] Fürnkranz, and Eyke [Akademischer Betreuer] Hüllermeier. "Efficient Decomposition-Based Multiclass and Multilabel Classification / Sang-Hyeun Park. Betreuer: Johannes Fürnkranz ; Eyke Hüllermeier." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2012. http://d-nb.info/1106115678/34.

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Eklund, Martin. "Comparing Feature Extraction Methods and Effects of Pre-Processing Methods for Multi-Label Classification of Textual Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231438.

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This thesis aims to investigate how different feature extraction methods applied to textual data affect the results of multi-label classification. Two different Bag of Words extraction methods are used, specifically the Count Vector and the TF-IDF approaches. A word embedding method is also investigated, called the GloVe extraction method. Multi-label classification can be useful for categorizing items, such as pieces of music or news articles, that may belong to multiple classes or topics. The effect of using different pre-processing methods is also investigated, such as the use of N-grams, s
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Books on the topic "Classification multilabels"

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. Multilabel Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. Multilabel Classification: Problem Analysis, Metrics and Techniques. Springer, 2016.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. Multilabel Classification: Problem Analysis, Metrics and Techniques. Springer London, Limited, 2016.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. Multilabel Classification: Problem Analysis, Metrics and Techniques. Springer, 2018.

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Book chapters on the topic "Classification multilabels"

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. "Introduction." In Multilabel Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_1.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. "Multilabel Classification." In Multilabel Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_2.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. "Case Studies and Metrics." In Multilabel Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_3.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. "Transformation-Based Classifiers." In Multilabel Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_4.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. "Adaptation-Based Classifiers." In Multilabel Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_5.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. "Ensemble-Based Classifiers." In Multilabel Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_6.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. "Dimensionality Reduction." In Multilabel Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_7.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. "Imbalance in Multilabel Datasets." In Multilabel Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_8.

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Herrera, Francisco, Francisco Charte, Antonio J. Rivera, and María J. del Jesus. "Multilabel Software." In Multilabel Classification. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41111-8_9.

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Kumar, Pawan. "DXML: Distributed Extreme Multilabel Classification." In Big Data Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93620-4_22.

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Conference papers on the topic "Classification multilabels"

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Xie, Sihong, Xiangnan Kong, Jing Gao, Wei Fan, and Philip S. Yu. "Multilabel Consensus Classification." In 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.97.

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Brinker, Christian, Eneldo Loza Mencia, and Johannes Furnkranz. "Graded Multilabel Classification by Pairwise Comparisons." In 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.102.

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Faiz, Farina. "Multilabel classification in human activity recognition." In SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems. ACM, 2020. http://dx.doi.org/10.1145/3384419.3430578.

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Gopal, Siddharth, and Yiming Yang. "Multilabel classification with meta-level features." In Proceeding of the 33rd international ACM SIGIR conference. ACM Press, 2010. http://dx.doi.org/10.1145/1835449.1835503.

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Mishra, Istasis, Arpan Dasgupta, Pratik Jawanpuria, Bamdev Mishra, and Pawan Kumar. "Light-weight Deep Extreme Multilabel Classification." In 2023 International Joint Conference on Neural Networks (IJCNN). IEEE, 2023. http://dx.doi.org/10.1109/ijcnn54540.2023.10191716.

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Cartwright, Mark, Ana Elisa Mendez Mendez, Jason Cramer, et al. "SONYC Urban Sound Tagging (SONYC-UST): A Multilabel Dataset from an Urban Acoustic Sensor Network." In 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2019). New York University, 2019. http://dx.doi.org/10.33682/j5zw-2t88.

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Bi, Wei, and James T. Kwok. "Hierarchical Multilabel Classification with Minimum Bayes Risk." In 2012 IEEE 12th International Conference on Data Mining (ICDM). IEEE, 2012. http://dx.doi.org/10.1109/icdm.2012.42.

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Ai, Xusheng, Jian Wu, Victor S. Sheng, Yufeng Yao, Pengpeng Zhao, and Zhiming Cui. "Best First Over-Sampling for Multilabel Classification." In CIKM'15: 24th ACM International Conference on Information and Knowledge Management. ACM, 2015. http://dx.doi.org/10.1145/2806416.2806634.

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Vasisht, Deepak, Andreas Damianou, Manik Varma, and Ashish Kapoor. "Active learning for sparse bayesian multilabel classification." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2014. http://dx.doi.org/10.1145/2623330.2623759.

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McDonald, Ryan, Koby Crammer, and Fernando Pereira. "Flexible text segmentation with structured multilabel classification." In the conference. Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1220575.1220699.

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