Results 11 to 20 of about 3,188,697 (327)

F1 Score Based Weighted Asynchronous Federated Learning

International Journal for Research in Applied Science and Engineering Technology
Abstract: The domain of federated learning has observed remarkable developments in recent years, enabling collaborative model training while preserving data privacy. This paper discusses several recent advancements in the field of federated learning, particularly in asynchronous and weighted federated learning.
Sneha Sree Yarlagadda, Sai Harshith Tule, Karthik Myada   +2 more
semanticscholar   +3 more sources

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, Kouji Yamamoto, Aya Kuchiba, Tatsuki Koyama   +3 more
semanticscholar   +5 more sources

Predicting breast cancer using machine learning classifiers and enhancing the output by combining the predictions to generate optimal F1-score

Biomedical and Biotechnology Research Journal, 2021
Background: Biomedical field has gained a lot of interest from active researchers today. Treating various diseases prevailing among the world has believed to bring huge insight in the today's research world.
Disha Harshadbhai Parekh, Vishal Dahiya
doaj   +4 more sources

Performance Metrics for Multilabel Emotion Classification: Comparing Micro, Macro, and Weighted F1-Scores

Applied Sciences
This study compares various F1-score variants—micro, macro, and weighted—to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotions. The
Maria Cristina Hinojosa Lee, Johan Braet, Johan Springael   +2 more
doaj   +4 more sources

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, Xiaoxue Zhao
openalex   +3 more sources

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, Jun Wang, Xiaoxue Zhao, Xiaoling Wang   +3 more
openalex   +3 more sources

sigmoidF1: A Smooth F1 Score Surrogate Loss for Multilabel Classification

, 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.).
G. F. Benedict, Vincent Koops, Daan Odijk, Maarten de Rijke   +3 more
  +7 more sources

Thresholding Classifiers to Maximize F1 Score

, 2014
This paper provides new insight into maximizing F1 scores in the context of binary classification and also in the context of multilabel classification. The harmonic mean of precision and recall, F1 score is widely used to measure the success of a binary classifier when one class is rare.
Zachary C. Lipton, Charles Elkan, N. Balakrishnan   +2 more
openalex   +4 more sources

Prediction of postoperative intensive care unit admission with artificial intelligence models in non-small cell lung carcinoma [PDF]

European Journal of Medical Research
Background There is no standard practice for intensive care admission after non-small cell lung cancer surgery. In this study, we aimed to determine the need for intensive care admission after non-small cell lung cancer surgery with deep learning models.
Gizem Özçıbık Işık, Burcu Kılıç, Ezel Erşen, Mehmet Kamil Kaynak, Akif Turna, Onur Sefa Özçıbık, Tülay Yıldırım, Hasan Volkan Kara   +7 more
doaj   +2 more sources

Fusion of Google Street View, LiDAR, and Orthophoto Classifications Using Ranking Classes Based on F1 Score for Building Land-Use Type Detection [PDF]

Remote Sensing
Building land-use type classification using earth observation data is essential for urban planning and emergency management. Municipalities usually do not hold a detailed record of building land-use types in their jurisdictions, and there is a significant need for a detailed classification of this data.
Nafiseh Ghasemian Sorboni, Jinfei Wang, Mohammad Reza Najafi   +2 more
openalex   +3 more sources