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Performance Evaluation of Machine Learning Classifiers for Memory Assessment Using EEG Signal
Published in Anand Sharma, Sunil Kumar Jangir, Manish Kumar, Dilip Kumar Choubey, Tarun Shrivastava, S. Balamurugan, Industrial Internet of Things, 2022
F1 Score is a performance measure, which considers both false positives and false negatives. It is the weighted average of Precision and Recall. For a model to be more accurate, the F1 Score should be higher. It can be stated mathematically as: Precision=2×Precision*RecallPrecision+Recall
A Visual Introduction to Machine Learning, AI Framework, and Architecture
Published in Vineet Kansal, Raju Ranjan, Sapna Sinha, Rajdev Tiwari, Nilmini Wickramasinghe, Healthcare and Knowledge Management for Society 5.0, 2021
Preeti Arora, Saksham Gera, Vinod M Kapse
The harmonic mean between precision and recall is called F1 Score. The range for F1 score is [0, 1]. F1 score tells how many instances are classified correctly as well as if a significant number of instances are missing. It also talks about the robustness of the classifier. Extremely accurate values are considered with high precision but lower recall. In that case, large missing instances are classified with great difficulty. A greater value for F1 score is considered to be better performance of the model. The mathematical expression is as follows: F1=211precission+1recall
A Review of Deep Learning Approaches for Plant Disease Detection and Classification
Published in Utku Kose, V. B. Surya Prasath, M. Rubaiyat Hossain Mondal, Prajoy Podder, Subrato Bharati, Artificial Intelligence and Smart Agriculture Technology, 2022
The F1-score, or simply F-score, is a measurement metric derived from the precision and recall metrics. F-score is defined as: F1-score=2*Recall*PrecisionRecall+Precision
A comprehensive comparison and analysis of machine learning algorithms including evaluation optimized for geographic location prediction based on Twitter tweets datasets
Published in Cogent Engineering, 2023
Hasti Samadi, Mohammed Ahsan Kollathodi
A higher value of recall and precision would essentially imply a higher f1-score and such a model can always be preferred. Provided the dataset that was implemented for creating the model is imbalanced, the f1-score can be a great metric to estimate the classifier performance. The f1-score would be a measure of the model’s accuracy on a dataset that initially evaluates binary classification systems classifying samples as either positive or negative. One of the major advantages of considering the f1-score is that it combines precision and recall into a single metric and as such it can be relevant while performing grid search or automated optimization and give better information about what the most optimal classifier will be for a particular data set. The comparison between different machine learning classifiers is given as follows (As shown in Figure 34 andFigure 35).
A new approach for super-resolution and classification applications on neonatal thermal images
Published in Quantitative InfraRed Thermography Journal, 2023
Fatih Mehmet Senalp, Murat Ceylan
In addition to the image quality metrics mentioned, unhealthy-healthy classification practices were carried out. Thus, the success of the SR images obtained was tested on a real application as well as PSNR and SSIM. In order to implement these applications, the classifier, which is CNN-based model, has been designed. In addition, transfer learning method was applied to this classifier by using pre-trained models and confusion matrices were obtained. Confusion matrices provide the necessary data to calculate classification metrics (accuracy, precision, recall, F1 score) values used to interpret the classification success [41]. The confusion matrix structure is shown in Figure 4. The equations of the accuracy, recall, precision and F1 score metrics are calculated using these values obtained from the confusion matrix are given in Table 2. While the precision shows how many predicted unhealthy babies are actually unhealthy, the recall shows how many babies who should be predicted as unhealthy are predicted as unhealthy. Also, the F1 score is expressed as the harmonic mean of the recall and precision metrics. Generally, in applications where the test dataset has an unbalanced distribution, the F1 score value is used because the accuracy value is insufficient to evaluate the classification success [42]. In this study, although the dataset has a balanced distribution, F1 score is used to check.
Covid-19 diagnosis by WE-SAJ
Published in Systems Science & Control Engineering, 2022
Wei Wang, Xin Zhang, Shui-Hua Wang, Yu-Dong Zhang
The WE-SAJ has improved accuracy by more than ten percentage points, which means our method has a higher practical value. More detailed performance improvements can be seen in the other performance indicators. WE-SAJ improves sensitivity by more than 12 percentage points and specificity by more than 11 percentage points. It suggests that WE-SAJ can reduce unnecessary healthcare resources by misdiagnosing as few healthy people as possible while ensuring that more infected people are correctly identified to identify COVID-19 patients effectively. It shows that WE-SAJ can ensure that more infected patients are correctly identified to effectively identify COVID-19 patients while minimising misdiagnosis of healthy people and reducing unnecessary wastage of healthcare resources. The improvement in F1-score also demonstrates that the model achieves better overall performance with equal weighting of precision and sensitivity. The increase of over 11 percentage points in the FMI index indicates the higher relevance of the data features extracted by the model to the data labels, which means that the model has improved its ability to extract useful features.