China 
Africa 
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Classification of Text Data in Healthcare Systems – A Comparative Study
Published in Om Prakash Jena, Bharat Bhushan, Nitin Rakesh, Parma Nand Astya, Yousef Farhaoui, Machine Learning and Deep Learning in Efficacy Improvement of Healthcare Systems, 2022
F-Measure (or F1 Score) compares models having different precision and recall values with a single evaluation measure. F-Measure can be defined as the harmonic mean of precision and recall, as shown: F−Measure(F1)=2∗(P×R)/(P+R)
Methods to Predict the Performance Analysis of Various Machine Learning Algorithms
Published in K Hemachandran, Shubham Tayal, Preetha Mary George, Parveen Singla, Utku Kose, Bayesian Reasoning and Gaussian Processes for Machine Learning Applications, 2022
M. Saritha, M. Lavanya, M. Narendra Reddy
F-score, which is also known as F1-score, is a performance evaluation measure for finding out the accuracy of a dataset (F-Score Definition | DeepAI, n.d.). F-score is basically used for the evaluation of the binary classification system, which is used to predict examples as being either “negative” or “positive.” The F-score is a popular metric for assessing data mining techniques, such as search results, as well as a variety of machine learning algorithms, particularly in natural language processing (Precision, Recall, and F Score Concepts in Details – Regenerative, n.d.). It is possible to tweak the F-score such that accuracy takes precedence over recall, or vice versa. The F0.5-score and the F2-score, and even the normal F1-score, are common modified F-scores. F1-score=2 * precision * recallprecision + recall
The KD-ORS Tree: An Efficient Indexing Technique for Content-Based Image Retrieval
Published in D. P. Acharjya, V. Santhi, Bio-Inspired Computing for Image and Video Processing, 2018
In simple terms, high recall means that an algorithm returned most of the relevant results, while high precision means that an algorithm returned substantially more relevant than irrelevant results. To evaluate the most efficient image retrieval, precision and recall scores are combined into a single measure of performance known as the F-score. Higher values of the F-score are obtained when both precision and recall are higher. Equation 13.19 is used to calculate the F-score. The accuracy is calculated as defined in Equation 13.20. F-Score=2∗P∗RP+R $$ \begin{aligned} F-Score&=2*\frac{P*R}{P + R}\end{aligned} $$ Accuracy=(P+R)2 $$ \begin{aligned} Accuracy&=\frac{(P+R)}{2} \end{aligned} $$
Towards POI-based large-scale land use modeling: spatial scale, semantic granularity, and geographic context
Published in International Journal of Digital Earth, 2023
Table 5 listed the f-scores of the supervised classification models trained with eight configurations. F-score is the harmonic mean of recall and precision and can thus account for both precision and recall as a model performance metric. As we can see from Table 5, the {filtered sample, no agg, all feature} configuration has the best overall performance score for open space and residential land use across all three selected geographic regions. For non-residential land use, {filtered sample, no agg, poi} configuration has the best f-score. In other words, incorporating non-POI geographic features into AOI embedding does not help classify non-residential land use. The performance differences vary across different geographic regions, with England having the biggest performance difference after adding non-POI geographic features and South Korea having almost the same performance.
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.
Sentiment mining in a collaborative learning environment: capitalising on big data
Published in Behaviour & Information Technology, 2019
Accuracy (A) is the most commonly used evaluation metric for the classifier performance (Ribeiro et al. 2016). The accuracy metric (TP + TN/TP + FP + FN) indicates that how many instances are correctly classified across all classes. Precision . represents how many of the instances classified as positive are actually positive. Recall is defined as how many cases that are actually positive cases are classified as positive. The F-Score is a metric of accuracy that combines both precision and recall. In this research, accuracy, precision, recall and F-scores were obtained for each classifier by averaging the values for all the classes.