This paper analyzes a generalization of a new met-ric to evaluate the classification performance in imbal-anced domains, combining some estimate of the overall accuracy with a plain index about how dominant the class with the highest individual accuracy is. A the-oretical analysis shows the merits of this metric when compared to other well-known measures. 1
In this thesis several sampling methods for Statistical Learning with imbalanced data have been impl...
Machine learning models may not be able to effectively learn and predict from imbalanced data in the...
<p>Evaluation of the performance of classification models on imbalance dataset using the G1 attribut...
This paper introduces a new metric, named Index of Balanced Accuracy, for evaluating learning proces...
In this contribution, the question of reporting performance of binary classifiers is opened in cont...
The area of imbalanced datasets is still relatively new, and it is known that the use of overall acc...
This paper introduces a framework that allows to mitigate the impact of class imbalance on most scal...
International audienceThe selection of the best classification algorithm for a given dataset is a ve...
<p>A metric requiring high F score as well as AUC-ROC provides a better measure of classification pe...
Abstract. Since the overall prediction error of a classifier on imbalanced problems can be potential...
Abstract. Performance metrics are used in various stages of the process aimed at solving a classific...
Abstract—Evaluating the performance of a classification algorithm critically requires a measure of t...
Abstract—This paper presents the theoretical research about the relationship between diversity of cl...
AbstractPerformance measures are used in various stages of the process aimed at solving a classifica...
[[abstract]]© 2007 Institute of Electrical and Electronics Engineers - In classification problems, t...
In this thesis several sampling methods for Statistical Learning with imbalanced data have been impl...
Machine learning models may not be able to effectively learn and predict from imbalanced data in the...
<p>Evaluation of the performance of classification models on imbalance dataset using the G1 attribut...
This paper introduces a new metric, named Index of Balanced Accuracy, for evaluating learning proces...
In this contribution, the question of reporting performance of binary classifiers is opened in cont...
The area of imbalanced datasets is still relatively new, and it is known that the use of overall acc...
This paper introduces a framework that allows to mitigate the impact of class imbalance on most scal...
International audienceThe selection of the best classification algorithm for a given dataset is a ve...
<p>A metric requiring high F score as well as AUC-ROC provides a better measure of classification pe...
Abstract. Since the overall prediction error of a classifier on imbalanced problems can be potential...
Abstract. Performance metrics are used in various stages of the process aimed at solving a classific...
Abstract—Evaluating the performance of a classification algorithm critically requires a measure of t...
Abstract—This paper presents the theoretical research about the relationship between diversity of cl...
AbstractPerformance measures are used in various stages of the process aimed at solving a classifica...
[[abstract]]© 2007 Institute of Electrical and Electronics Engineers - In classification problems, t...
In this thesis several sampling methods for Statistical Learning with imbalanced data have been impl...
Machine learning models may not be able to effectively learn and predict from imbalanced data in the...
<p>Evaluation of the performance of classification models on imbalance dataset using the G1 attribut...
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