Evaluation Measures for Models Assessment over Imbalanced Data Sets
作者： Taklit Akrouf Alitouche , Hassiba Kheliouane Djemaa , Mohamed Bekkar
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摘要: Imbalanced data learning is one of the challenging problems in mining; among this matter, founding right model assessment measures almost a primary research issue. Skewed class distribution causes misreading common evaluation as well it lead biased classification. This article presents set alternative for imbalanced assessment, using combined (G-means, likelihood ratios, Discriminant power, F-Measure Balanced Accuracy, Youden index, Matthews correlation coefficient), and graphical performance (ROC curve, Area Under Curve, Partial AUC, Weighted Cumulative Gains Curve lift chart, Lift AUL), that aim to provide more credible evaluation. We analyze applications these churn prediction models evaluation, known application Keywords: data, Model accuracy , G-means, F-Measure, coefficient, ROC, P-AUC,W-AUC, Lift, AUL
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