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The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. [PDF]
BMC Genomics, 2020 AbstractBackgroundTo evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet.
Chicco D, Jurman G.
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X-ray is not inferior to CT in terms of F1 score in the diagnosis of foreign body aspiration: a recall, precision and F1 score performance analysis based on bronchoscopically proven cases. [PDF]
J Pediatr (Rio J)In this study, we aimed to evaluate the diagnostic accuracy of X-ray and CT by using the F1 score with its non-inferiority margin in patients who underwent bronchoscopy with suspected diagnoses of foreign body aspiration (FBA).All children aged under 18 who underwent bronchoscopy with suspected diagnoses of FBA between June 2020 and December 2023 were ...
Sarac F, Yazici M.
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A Comprehensive Analysis on Detecting Chronic Kidney Disease by Employing Machine Learning Algorithms [PDF]
EAI Endorsed Transactions on Pervasive Health and Technology, 2021 INTRODUCTION: Chronic Kidney Disease refers to the slow, progressive deterioration of kidney functions. However, the impairment is irreversible and imperceptible up until the disease reaches one of the later stages ...
Mirza Nishat +7 more
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Evolving A Neural Network to Predict Diabetic Neuropathy [PDF]
EAI Endorsed Transactions on Scalable Information Systems, 2021 One of the main areas where machine learning (ML) techniques are used vastly is in prediction of diseases. Diabetic neuropathy (DN) disease is a complication of diabetes which causes damage to nerves.
Shiva Reddy, Gadiraju Mahesh, N. Preethi
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Towards an Online Empathy Assisted Counselling Web Application [PDF]
EAI Endorsed Transactions on Context-aware Systems and Applications, 2020 INTRODUCTION: In today's society mental health is becoming increasingly important. As a result, more and more individuals need guidance and counselling.
Aarif Mawani, Lawrence Nderu
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90% F1 Score in Relation Triple Extraction: Is it Real?
Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP, 2023 Accepted in GenBench workshop @ EMNLP ...
Saini, Pratik +3 more
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Confidence interval for micro-averaged F1 and macro-averaged F1 scores [PDF]
Applied Intelligence, 2021 AbstractA binary classification problem is common in medical field, and we often use sensitivity, specificity, accuracy, negative and positive predictive values as measures of performance of a binary predictor. In computer science, a classifier is usually evaluated with precision (positive predictive value) and recall (sensitivity). As a single summary
Kanae Takahashi +3 more
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Estimating the Uncertainty of Average F1 Scores [PDF]
Proceedings of the 2015 International Conference on The Theory of Information Retrieval, 2015 In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data.
Dell Zhang, Jun Wang 0012, Xiaoxue Zhao
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A Bayesian Hierarchical Model for Comparing Average F1 Scores [PDF]
2015 IEEE International Conference on Data Mining, 2015 In multi-class text classification, the performance (effectiveness) of a classifier is usually measured by micro-averaged and macro-averaged F1 scores. However, the scores themselves do not tell us how reliable they are in terms of forecasting the classifier's future performance on unseen data.
Dell Zhang +3 more
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sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification
Trans. Mach. Learn. Res., 2021 Multiclass multilabel classification is the task of attributing multiple labels to examples via predictions. Current models formulate a reduction of the multilabel setting into either multiple binary classifications or multiclass classification, allowing for the use of existing loss functions (sigmoid, cross-entropy, logistic, etc.).
Bénédict, G. +3 more
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