Results 1 to 10 of about 1,547,567 (330)
Sensors, 2021 Brain tumor classification plays an important role in clinical diagnosis and effective treatment. In this work, we propose a method for brain tumor classification using an ensemble of deep features and machine learning classifiers.
Jaeyong Kang +2 more
doaj +4 more sources
Copying Machine Learning Classifiers [PDF]
IEEE Access, 2020 We study copying of machine learning classifiers, an agnostic technique to replicate the decision behavior of any classifier. We develop the theory behind the problem of copying, highlighting its properties, and propose a framework to copy the decision ...
Irene Unceta, Jordi Nin, Oriol Pujol
doaj +5 more sources
Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations [PDF]
FAT*, 2019 Post-hoc explanations of machine learning models are crucial for people to understand and act on algorithmic predictions. An intriguing class of explanations is through counterfactuals, hypothetical examples that show people how to obtain a different ...
Dai Wuyang +4 more
core +2 more sources
Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study [PDF]
Journal of Medical Internet Research, 2020 BackgroundTwitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance.
Visweswaran, Shyam +8 more
doaj +3 more sources
Sentiment analysis of financial Twitter posts on Twitter with the machine learning classifiers. [PDF]
Heliyon, 2023 This paper presents a sentiment analysis combining the lexicon-based and machine learning (ML)-based approaches in Turkish to investigate the public mood for the prediction of stock market behavior in BIST30, Borsa Istanbul.
Cam H, Cam AV, Demirel U, Ahmed S.
europepmc +2 more sources
The impact of imputation quality on machine learning classifiers for datasets with missing values. [PDF]
Commun Med (Lond), 2023 Background Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods ...
Shadbahr T +18 more
europepmc +3 more sources
Machine learning magnetism classifiers from atomic coordinates
iScience, 2022 Summary: The determination of magnetic structure poses a long-standing challenge in condensed matter physics and materials science. Experimental techniques such as neutron diffraction are resource-limited and require complex structure refinement ...
Helena A. Merker +11 more
doaj +3 more sources
Remote Sensing, 2021 Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their ...
Shobitha Shetty +3 more
doaj +2 more sources
Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey [PDF]
ACM Journal on Responsible Computing, 2022 This article provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias mitigation for ML classifiers. These methods can be distinguished based
Max Hort +4 more
semanticscholar +1 more source
Optimised feature selection and cervical cancer prediction using Machine learning classification [PDF]
Scripta Medica, 2022 Background: Screening and early detection play a key role in cervical cancer prevention. The present study predicts the outcome of various diagnostic tests used to diagnose cervical cancer using machine learning algorithms. Methods: The present study ran
Tak Amit +3 more
doaj +1 more source