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Multi-label classification of COVID-19-related articles with an autoML approach

Ilija Tavchioski, Boshko Koloski, Blaž Škrlj, Senja Pollak
2022 Zenodo  
to the shared task titled LitCovid track Multi-label topic classification for COVID-19 literature annotation.  ...  Albeit the proposed system performed sub-optimally in terms of recall, it offered better-than-baseline (macro) precision, indication that automated representation learning is a promising approach to multilabel  ...  This paper is supported by European Union's Horizon 2020 research and innovation programme under grant agreement No 825153, project EMBEDDIA (Cross-Lingual Embeddings for Less-Represented Languages in  ... 
doi:10.5281/zenodo.5854553 fatcat:l5mnk6rncvawrbydmbj3hjca2y

A Comparison of Machine Learning Approaches for the Automated Classification of Dementia [chapter]

Herbert Jelinek, David Cornforth, Patricia Waley, Eduardo Fernandez, Wayne Robinson
2002 Lecture Notes in Computer Science  
Automated classification is a common goal of machine learning, and consists of assigning a class label to a set of measurements.  ...  Our findings demonstrate the utility of multi-fractal analysis combined with machine learning techniques in dementia research.  ...  Automated classification is a common goal of machine learning, and consists of assigning a class label to a set of measurements.  ... 
doi:10.1007/3-540-36187-1_70 fatcat:r3uiy7nujzg23c7ntzsadnddnq

Automated Multi-Label Classification based on ML-Plan [article]

Marcel Wever and Felix Mohr and Eyke Hüllermeier
2018 arXiv   pre-print
In particular, there is almost no work on automating the engineering of machine learning applications for multi-label classification. This paper makes two contributions.  ...  Automated machine learning (AutoML) has received increasing attention in the recent past.  ...  The resulting algorithm is called ML 2 -Plan, which stands for Planning for Multi Label Machine Learning.  ... 
arXiv:1811.04060v1 fatcat:t64odebc3rcazgzxqfvmeiksbe

Automatic Detection and Classification of Nutrients Deficiency in Fruit Based on Automated Machine Learning

2019 International Journal of Engineering and Advanced Technology  
In this paper, we propose solving fruit surface defect detection using Automated Machine Learning (AML).  ...  Machine learning-based classification and detection of surface defect of fruit involve manual feature identification and selection from input datasets.  ...  Automated Machine learning simplifies the task involved in deep learning application.  ... 
doi:10.35940/ijeat.a1029.109119 fatcat:mphmiv5r5baipcdsxxl3bzkkiu

Automatic Classification of Learning Objectives Based on Bloom's Taxonomy

Yuheng Li, Mladen Rakovic, Boon Xin Poh, Dragan Gasevic, Guanliang Chen, Antonija Mitrovic, Nigel Bosch
2022 Zenodo  
To remedy this challenge, we aimed to apply state-of-the-art computational techniques to automate the classification of learning objectives based on Bloom's taxonomy.  ...  Based on the labeled dataset, we applied five conventional machine learning approaches (i.e., naive Bayes, logistic regression, support vector machine, random forest, and XGBoost) and one deep learning  ...  Meanwhile, the multi-class multi-label BERT-based classifier performed better than all the binary and multiclass multi-label machine learning models from the same cognitive level, but rarely outperformed  ... 
doi:10.5281/zenodo.6853191 fatcat:r55uc3uglrgwhcgi7tmdxse5q4

Inter-Annotator Agreement in Sentiment Analysis: Machine Learning Perspective

Victoria Bobicev, Marina Sokolova
2017 RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning  
Although annotators work with limited parts of data sets, their results are extrapolated by automated text classification and affect the final classification results.  ...  We used several annotation agreement measures, as well as statistical analysis and Machine Learning to assess the resulting annotations.  ...  We apply Machine Learning (ML) techniques to compare human and automated recognition of sentiment labels.  ... 
doi:10.26615/978-954-452-049-6_015 dblp:conf/ranlp/BobicevS17 fatcat:ljngvucfz5aojozgcvgvnz56pu

Automated Tagging for the Retrieval of Software Resources in Grid and Cloud Infrastructures

Ioannis Katakis, George Pallis, Marios D. Dikaiakos, Onisiforos Onoufriou
2012 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)  
In order to achieve this, we model the problem of tag prediction as a multi-label classification problem.  ...  Using data extracted from production-quality Grid and Cloud computing infrastructures, we evaluate an important number of multi-label classifiers and discuss which one and with what settings is the most  ...  In order to provide automated tag assignments we formulated the problem as a machine learning multi-label classification task.  ... 
doi:10.1109/ccgrid.2012.66 dblp:conf/ccgrid/KatakisPDO12 fatcat:adito73jdbbhffkg72uyg24fem

Augmented Machine Learning Ensemble Extension Model for Social Media Health Trends Predictions

2019 International journal of recent technology and engineering  
The model is to use temporal datasets to deduce multi-label classification of health-related topics.  ...  Ensemble Learning wherein an array of various Machine Learning techniques can be employed to achieve better classification or clustering results.  ...  EM 1 and EM 2 in the Figure 4 below are two distinct ensemble machine learning models for multi-label text classification.  ... 
doi:10.35940/ijrte.b1091.0782s719 fatcat:f57k7bqqojg4vimz7vqrvjmxnu

A Comparative Study of Multi-Label Classification for Document Labeling in Ethical Protocol Review

Rizka Wakhidatus Sholikah, Diana Purwitasari, Mohammad Zaenuddin Hamidi
2022 Techno.Com  
Therefore, in this research, a comparative study was conducted on the problem of multi-label classification to automate the ethical protocol review process.  ...  The experiment results show that the use of the traditional machine learning approach produces better performance than the deep learning approach.  ...  Automatic labeling can be done using various methods for multi-label classification, both machine learning, and deep learning approaches.  ... 
doi:10.33633/tc.v21i2.5994 fatcat:3wpk5bsidbfd7mw45gindekr44

Automated machine learning approaches for emergency response and coordination via social media in the aftermath of a disaster: A review

Lokabhiram Dwarakanath, Amirrudin Kamsin, Rasheed Abubakar Rasheed, Anitha Anandhan, Liyana Shuib
2021 IEEE Access  
Many of those recent research articles discuss automated machine learning approaches to extract disaster, indicating posts useful for coordination from various social media posts.  ...  This review would help researchers in choosing further research topics pertaining to automated approaches for actionable information classification and disaster coordination and would help the emergency  ...  The annotations were utilized to test on a multi-label classification setting by using three well-known machine learning algorithms, Naive Bayes, Random Forest, and Support Vector Machines (SVM).  ... 
doi:10.1109/access.2021.3074819 fatcat:2ebym34ewjcgzn7im7gaac7udy

Automated Clinical Coding: What, Why, and Where We Are? [article]

Hang Dong, Matúš Falis, William Whiteley, Beatrice Alex, Joshua Matterson, Shaoxiong Ji, Jiaoyan Chen, Honghan Wu
2022 arXiv   pre-print
Knowledge-based methods that represent and reason the standard, explainable process of a task may need to be incorporated into deep learning-based methods for clinical coding.  ...  Our research reveals the gaps between the current deep learning-based approach applied to clinical coding and the need for explainability and consistency in real-world practice.  ...  For example, AKASA in the US is developing a deep-learning based solution, aiming to tackle automated clinical coding adapting a multi-label classification approach.  ... 
arXiv:2203.11092v2 fatcat:bx6n7hzulnhwbdhlxx6of55hde

Hierarchical learning for automated malware classification

Shayok Chakraborty, Jack W. Stokes, Lin Xiao, Dengyong Zhou, Mady Marinescu, Anil Thomas
2017 MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM)  
In this paper, we propose the novel idea and study the performance of employing hierarchical learning algorithms for automated classification of malicious files.  ...  Recently, anti-virus companies have started investing in machine learning solutions to augment signatures manually designed by analysts.  ...  Acknowledgment: The authors thank Dennis Batchelder for his support and guidance during this project.  ... 
doi:10.1109/milcom.2017.8170758 dblp:conf/milcom/ChakrabortySXZM17 fatcat:jydc5uok5reztlypmmzq3ll7s4

Multi-label classification approach for quranic verses labeling

Abdullahi Adeleke, Noor Azah Samsudin, Mohd Hisyam Abdul Rahim, Shamsul Kamal Ahmad Khalid, Riswan Efendi
2021 Indonesian Journal of Electrical Engineering and Computer Science  
Important among machine learning tasks is classification involving the process of training machines to make predictions from predefined labels.  ...  This research work presents an application of a multi-label classification (MLC) technique in automating Quranic verses labeling. MLC has been gaining attention in recent years.  ...  In this work, standard machine learning algorithms (classifiers) are applied for the multi-label task.  ... 
doi:10.11591/ijeecs.v24.i1.pp484-490 fatcat:qyclxllnhzfgrpwd2ohq7n6bae

Machine Learning for Melanoma Management in a Clinical Setting

Paul Walsh, Jennifer Lynch, Brian Kelly, Timothy Manning
2019 Collaborative European Research Conference  
Machine learning models trained using this data can serve the wider community for screening, diagnostic and prognostic purposes.  ...  As the data managed by this platform is structured and annotated, it is proposed that this could serve as a basis for supervised training datasets for machine learning.  ...  integrating Genomics with Electronic health records for Cancer CARE (SageCare), grant number 644186.  ... 
dblp:conf/cerc/WalshLKM19 fatcat:jjnoa5rbmzc7beeb5elnwnrhwq

PDF text classification to leverage information extraction from publication reports

Duy Duc An Bui, Guilherme Del Fiol, Siddhartha Jonnalagadda
2016 Journal of Biomedical Informatics  
Results-The multi-pass sieve algorithm achieved an accuracy of 92.6%, which was 9.7% (p<0.001) higher than the best performing machine learning classifier that used a logistic regression algorithm.  ...  In a two-step procedure, we evaluated (1) classification performance, and compared it with machine learning classifier, and (2) the effects of the algorithm on an IE system that extracts clinical outcome  ...  We investigated the rule-based multi-pass sieve approach and a set of machine learning approaches for automated text classification.  ... 
doi:10.1016/j.jbi.2016.03.026 pmid:27044929 pmcid:PMC4893911 fatcat:fwtdbiryb5hifbve3m4e2t6jha
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