The use of the area under the ROC curve in the evaluation of machine learning algorithms

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• SMOTE: Synthetic Minority Over-sampling Technique→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• An introduction to ROC analysis→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• The relationship between Precision-Recall and ROC curves→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• Support Vector Data Description→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• A systematic study of the class imbalance problem in convolutional neural networks→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• Exploratory Undersampling for Class-Imbalance Learning→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• Classification assessment methods→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification…→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• Using AUC and accuracy in evaluating learning algorithms→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• Machine learning predictive models for mineral prospectivity: An evaluation of neural net…→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• Mining with rarity→The use of the area under the ROC curve in the evaluation of machine learning algorithmsLegacy edge — source detail unavailable
• The use of the area under the ROC curve in the evaluation of machine learning algorithms→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
• The use of the area under the ROC curve in the evaluation of machine learning algorithms→Classification and Regression Trees.Legacy edge — source detail unavailable
• The use of the area under the ROC curve in the evaluation of machine learning algorithms→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• The use of the area under the ROC curve in the evaluation of machine learning algorithms→Introduction to Statistical Pattern RecognitionLegacy edge — source detail unavailable
• The use of the area under the ROC curve in the evaluation of machine learning algorithms→Signal detection theory and psychophysicsLegacy edge — source detail unavailable
• The use of the area under the ROC curve in the evaluation of machine learning algorithms→A method of comparing the areas under receiver operating characteristic curves derived fr…Legacy edge — source detail unavailable
• The use of the area under the ROC curve in the evaluation of machine learning algorithms→Introduction to statistical pattern recognition (2nd ed.)Legacy edge — source detail unavailable
• The use of the area under the ROC curve in the evaluation of machine learning algorithms→Pattern Recognition PrinciplesLegacy edge — source detail unavailable
• The use of the area under the ROC curve in the evaluation of machine learning algorithms→Pattern recognition : a statistical approachLegacy edge — source detail unavailable
• The use of the area under the ROC curve in the evaluation of machine learning algorithms→Improving the learning speed of 2-layer neural networks by choosing initial values of the…Legacy edge — source detail unavailable
• A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification…→Classification and Regression Trees.Legacy edge — source detail unavailable
• Mining with rarity→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
• Mining with rarity→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
• Mining with rarity→Classification and Regression Trees.Legacy edge — source detail unavailable
• Machine learning predictive models for mineral prospectivity: An evaluation of neural net…→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
• Machine learning predictive models for mineral prospectivity: An evaluation of neural net…→Classification and Regression Trees.Legacy edge — source detail unavailable
• Improving the learning speed of 2-layer neural networks by choosing initial values of the…→Learning Internal Representations by Error PropagationLegacy edge — source detail unavailable
• Using AUC and accuracy in evaluating learning algorithms→Signal detection theory and psychophysicsLegacy edge — source detail unavailable
• Using AUC and accuracy in evaluating learning algorithms→A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification…Legacy edge — source detail unavailable
• Using AUC and accuracy in evaluating learning algorithms→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
• A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Using AUC and accuracy in evaluating learning algorithmsLegacy edge — source detail unavailable
• A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→Exploratory Undersampling for Class-Imbalance LearningLegacy edge — source detail unavailable
• A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
• A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
• Exploratory Undersampling for Class-Imbalance Learning→Introduction to statistical pattern recognition (2nd ed.)Legacy edge — source detail unavailable
• Exploratory Undersampling for Class-Imbalance Learning→Mining with rarityLegacy edge — source detail unavailable
• Exploratory Undersampling for Class-Imbalance Learning→Introduction to Statistical Pattern RecognitionLegacy edge — source detail unavailable
• Exploratory Undersampling for Class-Imbalance Learning→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
• Exploratory Undersampling for Class-Imbalance Learning→Classification and Regression Trees.Legacy edge — source detail unavailable
• Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning→Mining with rarityLegacy edge — source detail unavailable
• Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
• Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
• SMOTE: Synthetic Minority Over-sampling Technique→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
• An introduction to ROC analysis→A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification…Legacy edge — source detail unavailable
• An introduction to ROC analysis→Classification and Regression Trees.Legacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→Mining with rarityLegacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→The relationship between Precision-Recall and ROC curvesLegacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→Using AUC and accuracy in evaluating learning algorithmsLegacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Ba…Legacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→Exploratory Undersampling for Class-Imbalance LearningLegacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→C4.5: Programs for Machine LearningLegacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets LearningLegacy edge — source detail unavailable
• An insight into classification with imbalanced data: Empirical results and current trends…→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
• A systematic study of the class imbalance problem in convolutional neural networks→Exploratory Undersampling for Class-Imbalance LearningLegacy edge — source detail unavailable
• A systematic study of the class imbalance problem in convolutional neural networks→Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets LearningLegacy edge — source detail unavailable
• A systematic study of the class imbalance problem in convolutional neural networks→SMOTE: Synthetic Minority Over-sampling TechniqueLegacy edge — source detail unavailable
• Classification assessment methods→A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification…Legacy edge — source detail unavailable
• Classification assessment methods→The relationship between Precision-Recall and ROC curvesLegacy edge — source detail unavailable
• Classification assessment methods→An introduction to ROC analysisLegacy edge — source detail unavailable
• Classification assessment methods→An insight into classification with imbalanced data: Empirical results and current trends…Legacy edge — source detail unavailable
• The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…→Using AUC and accuracy in evaluating learning algorithmsLegacy edge — source detail unavailable
• The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…→An introduction to ROC analysisLegacy edge — source detail unavailable
• The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy i…→Classification assessment methodsLegacy edge — source detail unavailable