This paper introduces a new metric, named Index of Balanced Accuracy, for evaluating learning processes in two-class imbalanced domains. The method combines an unbiased index of its overall accuracy and a measure about how dominant is the class with the highest individual accuracy rate. Some theoretical examples are conducted to illustrate the benefits of the new metric over other well-known performance measures. Finally, a number of experiments demonstrate the consistency and validity of the evaluation method here propose
he problem of modeling binary responses by using cross-sectional data has been addressed with a numb...
There are several aspects that might influence the performance achieved by existing learning systems...
Abstract—This paper presents the theoretical research about the relationship between diversity of cl...
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...
This paper analyzes a generalization of a new met-ric to evaluate the classification performance in ...
Since many important real-world classification problems involve learning from unbalanced data, the c...
Abstract. Many real world datasets exhibit skewed class distributions in which almost all instances ...
In this thesis several sampling methods for Statistical Learning with imbalanced data have been impl...
Class imbalance, as a phenomenon of asymmetry, has an adverse effect on the performance of most mach...
Abstract—Evaluating the performance of a classification algorithm critically requires a measure of t...
This paper introduces a framework that allows to mitigate the impact of class imbalance on most scal...
Abstract—In the last decade, class imbalance has attracted a huge amount of attention from researche...
Practitioners of data mining and machine learning have long observed that the imbalance of classes i...
The first book of its kind to review the current status and future direction of the exciting new bra...
he problem of modeling binary responses by using cross-sectional data has been addressed with a numb...
There are several aspects that might influence the performance achieved by existing learning systems...
Abstract—This paper presents the theoretical research about the relationship between diversity of cl...
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...
This paper analyzes a generalization of a new met-ric to evaluate the classification performance in ...
Since many important real-world classification problems involve learning from unbalanced data, the c...
Abstract. Many real world datasets exhibit skewed class distributions in which almost all instances ...
In this thesis several sampling methods for Statistical Learning with imbalanced data have been impl...
Class imbalance, as a phenomenon of asymmetry, has an adverse effect on the performance of most mach...
Abstract—Evaluating the performance of a classification algorithm critically requires a measure of t...
This paper introduces a framework that allows to mitigate the impact of class imbalance on most scal...
Abstract—In the last decade, class imbalance has attracted a huge amount of attention from researche...
Practitioners of data mining and machine learning have long observed that the imbalance of classes i...
The first book of its kind to review the current status and future direction of the exciting new bra...
he problem of modeling binary responses by using cross-sectional data has been addressed with a numb...
There are several aspects that might influence the performance achieved by existing learning systems...
Abstract—This paper presents the theoretical research about the relationship between diversity of cl...