<p>A metric requiring high F score as well as AUC-ROC provides a better measure of classification performance.</p
This paper analyzes a generalization of a new met-ric to evaluate the classification performance in ...
The performance of classification models is often measured using the metric, area under the curve (A...
<p>Evaluation of the performance of classification models on imbalance dataset using the G1 attribut...
AUC measure, which is used for classifier evaluation and represents one of the main tools of ROC ana...
The area under the ROC (Receiver Operating Characteristic) curve, or simply AUC, has been widely u...
: Optimal performance is desired for decision-making in any field with binary classifiers and diagno...
<p>Comparison of prediction performance of classifiers in terms of AUC score, at different levels hi...
<p>Comparisons of the classification performance of classic GS and MiNeGS approaches, using ROC AUC ...
Traditional measures for assessing the performance of classification models for binary outcomes are ...
Abstract. Performance metrics are used in various stages of the process aimed at solving a classific...
When examinees are classified into groups based on scores from educational assessment, two indices a...
In this contribution, the question of reporting performance of binary classifiers is opened in cont...
<p>The performance of different classifiers associated with the attribute selection methods assessed...
To evaluate the performance of text classifiers, we usually look at measures related to precision an...
Training classifiers using imbalanced data is a challenging problem in many real-world recognition a...
This paper analyzes a generalization of a new met-ric to evaluate the classification performance in ...
The performance of classification models is often measured using the metric, area under the curve (A...
<p>Evaluation of the performance of classification models on imbalance dataset using the G1 attribut...
AUC measure, which is used for classifier evaluation and represents one of the main tools of ROC ana...
The area under the ROC (Receiver Operating Characteristic) curve, or simply AUC, has been widely u...
: Optimal performance is desired for decision-making in any field with binary classifiers and diagno...
<p>Comparison of prediction performance of classifiers in terms of AUC score, at different levels hi...
<p>Comparisons of the classification performance of classic GS and MiNeGS approaches, using ROC AUC ...
Traditional measures for assessing the performance of classification models for binary outcomes are ...
Abstract. Performance metrics are used in various stages of the process aimed at solving a classific...
When examinees are classified into groups based on scores from educational assessment, two indices a...
In this contribution, the question of reporting performance of binary classifiers is opened in cont...
<p>The performance of different classifiers associated with the attribute selection methods assessed...
To evaluate the performance of text classifiers, we usually look at measures related to precision an...
Training classifiers using imbalanced data is a challenging problem in many real-world recognition a...
This paper analyzes a generalization of a new met-ric to evaluate the classification performance in ...
The performance of classification models is often measured using the metric, area under the curve (A...
<p>Evaluation of the performance of classification models on imbalance dataset using the G1 attribut...
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