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Journal articles on the topic 'Multilabel approach'

Author: Grafiati

Published: 4 June 2021

Last updated: 8 June 2024

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1

Li, Hua, Deyu Li, Yanhui Zhai, Suge Wang, and Jing Zhang. "A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/359626.

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Owing to the high dimensionality of multilabel data, feature selection in multilabel learning will be necessary in order to reduce the redundant features and improve the performance of multilabel classification. Rough set theory, as a valid mathematical tool for data analysis, has been widely applied to feature selection (also called attribute reduction). In this study, we propose a variable precision attribute reduct for multilabel data based on rough set theory, calledδ-confidence reduct, which can correctly capture the uncertainty implied among labels. Furthermore, judgement theory and disc

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Alzanin, Samah M., Abdu Gumaei, Md Azimul Haque, and Abdullah Y. Muaad. "An Optimized Arabic Multilabel Text Classification Approach Using Genetic Algorithm and Ensemble Learning." Applied Sciences 13, no. 18 (2023): 10264. http://dx.doi.org/10.3390/app131810264.

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Multilabel classification of Arabic text is an important task for understanding and analyzing social media content. It can enable the categorization and monitoring of social media posts, the detection of important events, the identification of trending topics, and the gaining of insights into public opinion and sentiment. However, multilabel classification of Arabic contents can present a certain challenge due to the high dimensionality of the representation and the unique characteristics of the Arabic language. In this paper, an effective approach is proposed for Arabic multilabel classificat

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Chen, Zhe, Hongli Zhang, Lin Ye, and Shang Li. "An Approach Based on Multilevel Convolution for Sentence-Level Element Extraction of Legal Text." Wireless Communications and Mobile Computing 2021 (December 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/1043872.

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In the judicial field, with the increase of legal text data, the extraction of legal text elements plays a more and more important role. In this paper, we propose a sentence-level model of legal text element extraction based on the structure of multilabel text classification. Our proposed model contains an encoder and an improved decoder. The encoder applies multilevel convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) as feature extraction networks to extract local neighborhood and context information from legal text, and a decoder applies LSTM with multiattention and full

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Bhattacharya, Debshree, and Manoj Kumar Nigam. "A Multilabel Approach for Fault Detection and Classification of Transmission Lines using Binary Relevance." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 7 (2023): 261–69. http://dx.doi.org/10.17762/ijritcc.v11i7.7934.

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In Contemporary automation systems, Fault detection and classification of electrical transmission lines in grid systems are given top priority. The broad application of Machine Learning (ML) methods has enabled the substitute of conventional methods of fault identification and classification. These methods are more effective ones that can identify faults early on using a significant quantity of sensory data. So detecting simultaneous failures is difficult in the context of distracting the noise and several faults in the transmission lines. This study contributes by offering a unique way for co

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Basu, Kaustav, Vincent Debusschere, Seddik Bacha, Ujjwal Maulik, and Sanghamitra Bondyopadhyay. "Nonintrusive Load Monitoring: A Temporal Multilabel Classification Approach." IEEE Transactions on Industrial Informatics 11, no. 1 (2015): 262–70. http://dx.doi.org/10.1109/tii.2014.2361288.

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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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Lee, Jaesung, and Dae-Won Kim. "Scalable Multilabel Learning Based on Feature and Label Dimensionality Reduction." Complexity 2018 (September 24, 2018): 1–15. http://dx.doi.org/10.1155/2018/6292143.

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The data-driven management of real-life systems based on a trained model, which in turn is based on the data gathered from its daily usage, has attracted a lot of attention because it realizes scalable control for large-scale and complex systems. To obtain a model within an acceptable computational cost that is restricted by practical constraints, the learning algorithm may need to identify essential data that carries important knowledge on the relation between the observed features representing the measurement value and labels encoding the multiple target concepts. This results in an increase

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Do, Kien, Truyen Tran, Thin Nguyen, and Svetha Venkatesh. "Attentional multilabel learning over graphs: a message passing approach." Machine Learning 108, no. 10 (2019): 1757–81. http://dx.doi.org/10.1007/s10994-019-05782-6.

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Valverde-Albacete, Francisco J., and Carmen Peláez-Moreno. "A Formalization of Multilabel Classification in Terms of Lattice Theory and Information Theory: Concerning Datasets." Mathematics 12, no. 2 (2024): 346. http://dx.doi.org/10.3390/math12020346.

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Multilabel classification is a recently conceptualized task in machine learning. Contrary to most of the research that has so far focused on classification machinery, we take a data-centric approach and provide an integrative framework that blends qualitative and quantitative descriptions of multilabel data sources. By combining lattice theory, in the form of formal concept analysis, and entropy triangles, obtained from information theory, we explain from first principles the fundamental issues of multilabel datasets such as the dependencies of the labels, their imbalances, or the effects of t

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Sakr, Nehal A., Mervat Abu-ElKheir, A. Atwan, and H. H. Soliman. "A multilabel classification approach for complex human activities using a combination of emerging patterns and fuzzy sets." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4 (2019): 2993. http://dx.doi.org/10.11591/ijece.v9i4.pp2993-3001.

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In our daily lives, humans perform different Activities of Daily Living (ADL), such as cooking, and studying. According to the nature of humans, they perform these activities in a sequential/simple or an overlapping/complex scenario. Many research attempts addressed simple activity recognition, but complex activity recognition is still a challenging issue. Recognition of complex activities is a multilabel classification problem, such that a test instance is assigned to a multiple overlapping activities. Existing data-driven techniques for complex activity recognition can recognize a maximum nu

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Tai, Farbound, and Hsuan-Tien Lin. "Multilabel Classification with Principal Label Space Transformation." Neural Computation 24, no. 9 (2012): 2508–42. http://dx.doi.org/10.1162/neco_a_00320.

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We consider a hypercube view to perceive the label space of multilabel classification problems geometrically. The view allows us not only to unify many existing multilabel classification approaches but also design a novel algorithm, principal label space transformation (PLST), that captures key correlations between labels before learning. The simple and efficient PLST relies on only singular value decomposition as the key step. We derive the theoretical guarantee of PLST and evaluate its empirical performance using real-world data sets. Experimental results demonstrate that PLST is faster than

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Li, Yongchao. "Application of Uncertainty Thought Environment in Judicial Adjudication Based on Cognitive Psychology." Journal of Environmental and Public Health 2022 (September 6, 2022): 1–9. http://dx.doi.org/10.1155/2022/1088046.

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The uncertainty of judicial decision-making has a deep and extensive theoretical foundation. Theoretical analysis starts with a reflection on legal rationalism that challenges the legal certainty before delving deeply into the case’s facts and the entire legal system. In light of this, this paper explores a novel approach to enhance the reasoning mechanism of trial documents from the viewpoint of modern cognitive psychology, concentrating on the parties’ and the public’s cognitive processes to justice. It is suggested to use an inert hierarchical multilabel classification algorithm. In order t

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Qingyao Wu, Yunming Ye, Haijun Zhang, Tommy W. S. Chow, and Shen-Shyang Ho. "ML-TREE: A Tree-Structure-Based Approach to Multilabel Learning." IEEE Transactions on Neural Networks and Learning Systems 26, no. 3 (2015): 430–43. http://dx.doi.org/10.1109/tnnls.2014.2315296.

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Zhang, Xiao, Wenzhong Li, Haochao Ying, Feng Li, Siyi Tang, and Sanglu Lu. "Emotion Detection in Online Social Networks: A Multilabel Learning Approach." IEEE Internet of Things Journal 7, no. 9 (2020): 8133–43. http://dx.doi.org/10.1109/jiot.2020.3004376.

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Yélamos, Ignacio, Moisès Graells, Luis Puigjaner, and Gerard Escudero. "Simultaneous fault diagnosis in chemical plants using a multilabel approach." AIChE Journal 53, no. 11 (2007): 2871–84. http://dx.doi.org/10.1002/aic.11313.

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Bhamare, Bhavana R., and Jeyanthi Prabhu. "A Multilabel Classifier for Text Classification and Enhanced BERT System." Revue d'Intelligence Artificielle 35, no. 2 (2021): 167–76. http://dx.doi.org/10.18280/ria.350209.

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Now-a-day, a vast variety of reviews are published on the web. As a result, an automated system to analyze and extract knowledge from such textual data is needed. Sentiment analysis is a well-known sub-area in Natural Language Processing (NLP). In earlier research, sentiments were determined without considering the aspects specified in a review instance. Aspect-based sentiment analysis (ABSA) has caught the attention of researchers. Many existing systems consider ABSA as a single label classification problem. This drawback is handled in this study by proposing three approaches that use multila

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Jiang, Nan-Jiang, and Marie-Catherine de Marneffe. "Investigating Reasons for Disagreement in Natural Language Inference." Transactions of the Association for Computational Linguistics 10 (2022): 1357–74. http://dx.doi.org/10.1162/tacl_a_00523.

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Abstract We investigate how disagreement in natural language inference (NLI) annotation arises. We developed a taxonomy of disagreement sources with 10 categories spanning 3 high- level classes. We found that some disagreements are due to uncertainty in the sentence meaning, others to annotator biases and task artifacts, leading to different interpretations of the label distribution. We explore two modeling approaches for detecting items with potential disagreement: a 4-way classification with a “Complicated” label in addition to the three standard NLI labels, and a multilabel classification a

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Nanni, Loris, Luca Trambaiollo, Sheryl Brahnam, Xiang Guo, and Chancellor Woolsey. "Ensemble of Networks for Multilabel Classification." Signals 3, no. 4 (2022): 911–31. http://dx.doi.org/10.3390/signals3040054.

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Multilabel learning goes beyond standard supervised learning models by associating a sample with more than one class label. Among the many techniques developed in the last decade to handle multilabel learning best approaches are those harnessing the power of ensembles and deep learners. This work proposes merging both methods by combining a set of gated recurrent units, temporal convolutional neural networks, and long short-term memory networks trained with variants of the Adam optimization approach. We examine many Adam variants, each fundamentally based on the difference between present and

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Yang, Bo, Kunkun Tong, Xueqing Zhao, Shanmin Pang, and Jinguang Chen. "Multilabel Classification Using Low-Rank Decomposition." Discrete Dynamics in Nature and Society 2020 (April 7, 2020): 1–8. http://dx.doi.org/10.1155/2020/1279253.

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In the multilabel learning framework, each instance is no longer associated with a single semantic, but rather with concept ambiguity. Specifically, the ambiguity of an instance in the input space means that there are multiple corresponding labels in the output space. In most of the existing multilabel classification methods, a binary annotation vector is used to denote the multiple semantic concepts. That is, +1 denotes that the instance has a relevant label, while −1 means the opposite. However, the label representation contains too little semantic information to truly express the difference

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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

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Raj N, Nijil, and T. Mahalekshmi. "Multilabel Classification of Membrane Protein in Human by Decision Tree (DT) Approach." Biomedical and Pharmacology Journal 11, no. 1 (2018): 113–21. http://dx.doi.org/10.13005/bpj/1353.

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Multi-label classification methods are important in various fields,such as protein type,protein function, semantic scene classification and music categorization . In multi-label classification, each sample can be associated with a set of class labels. In protein type classification, one of the major types of protein is membrane protein. The Membrane proteins are performing different cellular processes and important functions, which are based on the protein types. Each membrane protein have different rolls at the same time. In this study we proposes membrane protein type classification using De

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V., R., and Disha Rajan. "New Approach for Joint Multilabel Classification with Community-Aware Label Graph Learning Technique." International Journal of Computer Applications 180, no. 36 (2018): 1–7. http://dx.doi.org/10.5120/ijca2018916892.

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He, Zhiyang, Ji Wu, and Tao Li. "Label Correlation Mixture Model: A Supervised Generative Approach to Multilabel Spoken Document Categorization." IEEE Transactions on Emerging Topics in Computing 3, no. 2 (2015): 235–45. http://dx.doi.org/10.1109/tetc.2014.2377559.

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Tharmakulasingam, Mukunthan, Brian Gardner, Roberto La Ragione, and Anil Fernando. "Explainable Deep Learning Approach for Multilabel Classification of Antimicrobial Resistance With Missing Labels." IEEE Access 10 (2022): 113073–85. http://dx.doi.org/10.1109/access.2022.3216896.

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Wang, Qian, and Mingzhe Wang. "Aerobics Action Recognition Algorithm Based on Three-Dimensional Convolutional Neural Network and Multilabel Classification." Scientific Programming 2021 (July 3, 2021): 1–8. http://dx.doi.org/10.1155/2021/3058141.

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In the context of modern people increasingly paying attention to health and promoting aerobics, the amount of data and audiences of aerobics videos has grown rapidly, and its potential application value has attracted widespread attention from scientific research and industry perspectives. This article has integrated computer vision and deep learning related knowledge to realize the intelligent recognition and representation of specific human movements in aerobics video sequences. The study proposes an automatic recognition method for floor exercise videos based on three-dimensional convolution

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Manchanda, Saurav, and George Karypis. "CAWA: An Attention-Network for Credit Attribution." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8472–79. http://dx.doi.org/10.1609/aaai.v34i05.6367.

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Credit attribution is the task of associating individual parts in a document with their most appropriate class labels. It is an important task with applications to information retrieval and text summarization. When labeled training data is available, traditional approaches for sequence tagging can be used for credit attribution. However, generating such labeled datasets is expensive and time-consuming. In this paper, we present Credit Attribution With Attention (CAWA), a neural-network-based approach, that instead of using sentence-level labeled data, uses the set of class labels that are asso

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Soleimani, Hossein, and David J. Miller. "Semisupervised, Multilabel, Multi-Instance Learning for Structured Data." Neural Computation 29, no. 4 (2017): 1053–102. http://dx.doi.org/10.1162/neco_a_00939.

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Many classification tasks require both labeling objects and determining label associations for parts of each object. Example applications include labeling segments of images or determining relevant parts of a text document when the training labels are available only at the image or document level. This task is usually referred to as multi-instance (MI) learning, where the learner typically receives a collection of labeled (or sometimes unlabeled) bags, each containing several segments (instances). We propose a semisupervised MI learning method for multilabel classification. Most MI learning me

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Pachet, F., and P. Roy. "Improving Multilabel Analysis of Music Titles: A Large-Scale Validation of the Correction Approach." IEEE Transactions on Audio, Speech, and Language Processing 17, no. 2 (2009): 335–43. http://dx.doi.org/10.1109/tasl.2008.2008734.

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Roy, Kamol Chandra, Samiul Hasan, and Pallab Mozumder. "A multilabel classification approach to identify hurricane‐induced infrastructure disruptions using social media data." Computer-Aided Civil and Infrastructure Engineering 35, no. 12 (2020): 1387–402. http://dx.doi.org/10.1111/mice.12573.

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Yang, Wenyuan, Chan Li, and Hong Zhao. "Label Distribution Learning by Regularized Sample Self-Representation." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/1090565.

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Multilabel learning that focuses on an instance of the corresponding related or unrelated label can solve many ambiguity problems. Label distribution learning (LDL) reflects the importance of the related label to an instance and offers a more general learning framework than multilabel learning. However, the current LDL algorithms ignore the linear relationship between the distribution of labels and the feature. In this paper, we propose a regularized sample self-representation (RSSR) approach for LDL. First, the label distribution problem is formalized by sample self-representation, whereby ea

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Chen, Yang, and Bowen Shi. "Enhanced Heterogeneous Graph Attention Network with a Novel Multilabel Focal Loss for Document-Level Relation Extraction." Entropy 26, no. 3 (2024): 210. http://dx.doi.org/10.3390/e26030210.

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Recent years have seen a rise in interest in document-level relation extraction, which is defined as extracting all relations between entities in multiple sentences of a document. Typically, there are multiple mentions corresponding to a single entity in this context. Previous research predominantly employed a holistic representation for each entity to predict relations, but this approach often overlooks valuable information contained in fine-grained entity mentions. We contend that relation prediction and inference should be grounded in specific entity mentions rather than abstract entity con

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Qi, Guanpeng, Ze Xu, Hanyu Dan, et al. "A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris." Computational Intelligence and Neuroscience 2022 (December 23, 2022): 1–19. http://dx.doi.org/10.1155/2022/5852089.

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MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods usin

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Woźniak, Rafał, Piotr Ożdżyński, and Danuta Zakrzewska. "CLUSTER ANALYSIS OF MEDICAL TEXT DOCUMENTS BY USING SEMI-CLUSTERING APPROACH BASED ON GRAPH REPRESENTATION." Information System in Management 7, no. 3 (2018): 213–24. http://dx.doi.org/10.22630/isim.2018.7.3.19.

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The development of Internet resulted in an increasing number of online text re-positories. In many cases, documents are assigned to more than one class and automatic multi-label classification needs to be used. When the number of labels exceeds the number of the documents, effective label space dimension reduction may signifi-cantly improve classification accuracy, what is a major priority in the medical field. In the paper, we propose document clustering for label selection. We use semi-clustering method, by considering graph representation, where documents are represented by vertices and edg

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Massidda, Luca, Marino Marrocu, and Simone Manca. "Non-Intrusive Load Disaggregation by Convolutional Neural Network and Multilabel Classification." Applied Sciences 10, no. 4 (2020): 1454. http://dx.doi.org/10.3390/app10041454.

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Non-intrusive load monitoring (NILM) is the main method used to monitor the energy footprint of a residential building and disaggregate total electrical usage into appliance-related signals. The most common disaggregation algorithms are based on the Hidden Markov Model, while solutions based on deep neural networks have recently caught the attention of researchers. In this work we address the problem through the recognition of the state of activation of the appliances using a fully convolutional deep neural network, borrowing some techniques used in the semantic segmentation of images and mult

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Mehrang, Saeed, Olli Lahdenoja, Matti Kaisti, et al. "Classification of Atrial Fibrillation and Acute Decompensated Heart Failure Using Smartphone Mechanocardiography: A Multilabel Learning Approach." IEEE Sensors Journal 20, no. 14 (2020): 7957–68. http://dx.doi.org/10.1109/jsen.2020.2981334.

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Wang, Shuoyao, Suzhi Bi, and Ying-Jun Angela Zhang. "Locational Detection of the False Data Injection Attack in a Smart Grid: A Multilabel Classification Approach." IEEE Internet of Things Journal 7, no. 9 (2020): 8218–27. http://dx.doi.org/10.1109/jiot.2020.2983911.

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Aziz, Khoirul, Inggis Kurnia Trisiawan, Kadek Dwi Suyasmini, Zendi Iklima, and Mirna Yunita. "Multilabel image analysis on Polyethylene Terephthalate bottle images using PETNet Convolution Architecture." SINERGI 27, no. 2 (2023): 163. http://dx.doi.org/10.22441/sinergi.2023.2.003.

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Packaging is one of the important aspects of the product. Good packaging can increase the competitiveness of a product. Therefore, to maintain the quality of the packaging of a product, it is necessary to have a visual inspection. Furthermore, an automatic visual inspection can reduce the occurrence of human errors in the manual inspection process. This research will use the convolution network to detect and classify PET (Polyethylene Terephthalate) bottles. The Convolutional Neural Network (CNN) method is one approach that can be used to detect and classify PET bottle packaging. This research

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Cheng, Yi-Hsuan, Margaret Lech, and Richardt Howard Wilkinson. "Simultaneous Sleep Stage and Sleep Disorder Detection from Multimodal Sensors Using Deep Learning." Sensors 23, no. 7 (2023): 3468. http://dx.doi.org/10.3390/s23073468.

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Sleep scoring involves the inspection of multimodal recordings of sleep data to detect potential sleep disorders. Given that symptoms of sleep disorders may be correlated with specific sleep stages, the diagnosis is typically supported by the simultaneous identification of a sleep stage and a sleep disorder. This paper investigates the automatic recognition of sleep stages and disorders from multimodal sensory data (EEG, ECG, and EMG). We propose a new distributed multimodal and multilabel decision-making system (MML-DMS). It comprises several interconnected classifier modules, including deep

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Ge, Hongwei, Zehang Yan, Jing Dou, Zhen Wang, and ZhiQiang Wang. "A Semisupervised Framework for Automatic Image Annotation Based on Graph Embedding and Multiview Nonnegative Matrix Factorization." Mathematical Problems in Engineering 2018 (June 27, 2018): 1–11. http://dx.doi.org/10.1155/2018/5987906.

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Automatic image annotation is for more accurate image retrieval and classification by assigning labels to images. This paper proposes a semisupervised framework based on graph embedding and multiview nonnegative matrix factorization (GENMF) for automatic image annotation with multilabel images. First, we construct a graph embedding term in the multiview NMF based on the association diagrams between labels for semantic constraints. Then, the multiview features are fused and dimensions are reduced based on multiview NMF algorithm. Finally, image annotation is achieved by using the new features t

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Xu, Shuo, Yuefu Zhang, Xin An, and Sainan Pi. "Performance evaluation of seven multi-label classification methods on real-world patent and publication datasets." Journal of Data and Information Science 9, no. 2 (2024): 81–103. http://dx.doi.org/10.2478/jdis-2024-0014.

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Abstract Purpose Many science, technology and innovation (STI) resources are attached with several different labels. To assign automatically the resulting labels to an interested instance, many approaches with good performance on the benchmark datasets have been proposed for multilabel classification task in the literature. Furthermore, several open-source tools implementing these approaches have also been developed. However, the characteristics of real-world multilabel patent and publication datasets are not completely in line with those of benchmark ones. Therefore, the main purpose of this

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Alamsyah, Andry, and Nadhif Ditertian Girawan. "Improving Clothing Product Quality and Reducing Waste Based on Consumer Review Using RoBERTa and BERTopic Language Model." Big Data and Cognitive Computing 7, no. 4 (2023): 168. http://dx.doi.org/10.3390/bdcc7040168.

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The disposability of clothing has emerged as a critical concern, precipitating waste accumulation due to product quality degradation. Such consequences exert significant pressure on resources and challenge sustainability efforts. In response, this research focuses on empowering clothing companies to elevate product excellence by harnessing consumer feedback. Beyond insights, this research extends to sustainability by providing suggestions on refining product quality by improving material handling, gradually mitigating waste production, and cultivating longevity, therefore decreasing discarded

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Liu, Ziqing, Haiyang He, Shixing Yan, Yong Wang, Tao Yang, and Guo-Zheng Li. "End-to-End Models to Imitate Traditional Chinese Medicine Syndrome Differentiation in Lung Cancer Diagnosis: Model Development and Validation." JMIR Medical Informatics 8, no. 6 (2020): e17821. http://dx.doi.org/10.2196/17821.

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Background Traditional Chinese medicine (TCM) has been shown to be an efficient mode to manage advanced lung cancer, and accurate syndrome differentiation is crucial to treatment. Documented evidence of TCM treatment cases and the progress of artificial intelligence technology are enabling the development of intelligent TCM syndrome differentiation models. This is expected to expand the benefits of TCM to lung cancer patients. Objective The objective of this work was to establish end-to-end TCM diagnostic models to imitate lung cancer syndrome differentiation. The proposed models used unstruct

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Huang, Guan-Hua, Qi-Jia Fu, Ming-Zhang Gu, Nan-Han Lu, Kuo-Ying Liu, and Tai-Been Chen. "Deep Transfer Learning for the Multilabel Classification of Chest X-ray Images." Diagnostics 12, no. 6 (2022): 1457. http://dx.doi.org/10.3390/diagnostics12061457.

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Chest X-ray (CXR) is widely used to diagnose conditions affecting the chest, its contents, and its nearby structures. In this study, we used a private data set containing 1630 CXR images with disease labels; most of the images were disease-free, but the others contained multiple sites of abnormalities. Here, we used deep convolutional neural network (CNN) models to extract feature representations and to identify possible diseases in these images. We also used transfer learning combined with large open-source image data sets to resolve the problems of insufficient training data and optimize the

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Wang, Zhao, Cuiqing Jiang, and Huimin Zhao. "Depicting Risk Profile over Time: A Novel Multiperiod Loan Default Prediction Approach." MIS Quarterly 47, no. 4 (2023): 1455–86. http://dx.doi.org/10.25300/misq/2022/17491.

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With the rapid development of fintech, the need for dynamic credit risk evaluation is becoming increasingly important. While previous studies on credit scoring have mostly focused on single-period loan default prediction, we call for a new avenue—multiperiod default prediction (MPDP)—to depict risk profiles over time. To address the challenges raised by MPDP, such as monotonic default probability prediction and complex relationship accommodation, we propose a novel approach, hybrid and collective scoring (HACS). We design a hybrid modeling strategy to predict whether and when a borrower will d

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Benyahia, Zakaria, Mostafa Hefnawi, Mohamed Aboulfatah, Hassan Abdelmounim, and Taoufiq Gadi. "A Two-Stage Support Vector Machine and SqueezeNet System for Range-Angle and Range-Speed Estimation in a Cluttered Environment of Automotive MIMO Radar Systems." ITM Web of Conferences 48 (2022): 01010. http://dx.doi.org/10.1051/itmconf/20224801010.

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This paper proposes a two-stage deep-learning approach for frequency modulated continuous waveform multiple‐input multiple‐output (FMCW MIMO) radar embedded in cluttered and jammed environments. The first stage uses the support vector machine (SVM) as a feature extractor that discriminates targets from clutters and jammers. In the second stage, the angle, range, and Doppler estimations of the extracted targets are treated by the SqueezeNet deep convolutional neural network (DCNN) as a multilabel classification problem. The performance of the proposed hybrid SVM-SqueezeNet method is very close

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Wattanapornprom, Warin, Chinae Thammarongtham, Apiradee Hongsthong, and Supatcha Lertampaiporn. "Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization." Life 11, no. 4 (2021): 293. http://dx.doi.org/10.3390/life11040293.

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The accurate prediction of protein localization is a critical step in any functional genome annotation process. This paper proposes an improved strategy for protein subcellular localization prediction in plants based on multiple classifiers, to improve prediction results in terms of both accuracy and reliability. The prediction of plant protein subcellular localization is challenging because the underlying problem is not only a multiclass, but also a multilabel problem. Generally, plant proteins can be found in 10–14 locations/compartments. The number of proteins in some compartments (nucleus,

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Melo, Andre, Johanna Völker, and Heiko Paulheim. "Type Prediction in Noisy RDF Knowledge Bases Using Hierarchical Multilabel Classification with Graph and Latent Features." International Journal on Artificial Intelligence Tools 26, no. 02 (2017): 1760011. http://dx.doi.org/10.1142/s0218213017600119.

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Semantic Web knowledge bases, in particular large cross-domain data, are often noisy, incorrect, and incomplete with respect to type information. This incompleteness can be reduced, as previous work shows, with automatic type prediction methods. Most knowledge bases contain an ontology defining a type hierarchy, and, in general, entities are allowed to have multiple types (classes of an instance assigned with the rdf:type relation). In this paper, we exploit these characteristics and formulate the type prediction problem as hierarchical multi classification, where the labels are types. We eval

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Takeoka, Kunihiro, Masafumi Oyamada, Shinji Nakadai, and Takeshi Okadome. "Meimei: An Efficient Probabilistic Approach for Semantically Annotating Tables." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 281–88. http://dx.doi.org/10.1609/aaai.v33i01.3301281.

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Given a large amount of table data, how can we find the tables that contain the contents we want? A naive search fails when the column names are ambiguous, such as if columns containing stock price information are named “Close” in one table and named “P” in another table.One way of dealing with this problem that has been gaining attention is the semantic annotation of table data columns by using canonical knowledge. While previous studies successfully dealt with this problem for specific types of table data such as web tables, it still remains for various other types of table data: (1) most ap

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Chen, Jinzhao, Gaoyu Wu, Andrew Michelson, et al. "Mining reported adverse events induced by potential opioid-drug interactions." JAMIA Open 3, no. 1 (2020): 104–12. http://dx.doi.org/10.1093/jamiaopen/ooz073.

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Abstract Objective Opioid-based analgesia is routinely used in clinical practice for the management of pain and alleviation of suffering at the end of life. It is well-known that opioid-based medications can be highly addictive, promoting not only abuse but also life-threatening overdoses. The scope of opioid-related adverse events (AEs) beyond these well-known effects remains poorly described. This exploratory analysis investigates potential AEs from drug-drug interactions between opioid and nonopioid medications (ODIs). Materials and Methods In this study, we conduct an initial exploration o

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Hossain, Prommy Sultana, Kyungsup Kim, Jia Uddin, Md Abdus Samad, and Kwonhue Choi. "Enhancing Taxonomic Categorization of DNA Sequences with Deep Learning: A Multi-Label Approach." Bioengineering 10, no. 11 (2023): 1293. http://dx.doi.org/10.3390/bioengineering10111293.

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The application of deep learning for taxonomic categorization of DNA sequences is investigated in this study. Two deep learning architectures, namely the Stacked Convolutional Autoencoder (SCAE) with Multilabel Extreme Learning Machine (MLELM) and the Variational Convolutional Autoencoder (VCAE) with MLELM, have been proposed. These designs provide precise feature maps for individual and inter-label interactions within DNA sequences, capturing their spatial and temporal properties. The collected features are subsequently fed into MLELM networks, which yield soft classification scores and hard

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