2018
DOI: 10.1016/j.ins.2018.06.020
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Visual-based analysis of classification measures and their properties for class imbalanced problems

Abstract: With a plethora of available classification performance measures, choosing the right metric for the right task requires careful thought. To make this decision in an informed manner, one should study and compare general properties of candidate measures. However, analysing measures with respect to complete ranges of their domain values is a difficult and challenging task. In this study, we attempt to support such analyses with a specialized visualization technique, which operates in a barycentric coordinate syst… Show more

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Cited by 41 publications
(23 citation statements)
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References 32 publications
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“…Three well-known machine learning prediction models are used to explore the effect of users' platforms on their purchase intention behaviours. These (Brzezinski et al, 2018), (Bader-El-Den et al, 2018). Furthermore, in order to validate the proposed approach and evaluate the ability of the machine learning prediction models, we run experiments with ten cross-validations.…”
Section: Experiments Results and Discussionmentioning
confidence: 99%
“…Three well-known machine learning prediction models are used to explore the effect of users' platforms on their purchase intention behaviours. These (Brzezinski et al, 2018), (Bader-El-Den et al, 2018). Furthermore, in order to validate the proposed approach and evaluate the ability of the machine learning prediction models, we run experiments with ten cross-validations.…”
Section: Experiments Results and Discussionmentioning
confidence: 99%
“…G-mean is the geometric mean of recall (TPR) and specificity (True Negative Rate-TNR), defined as G mean = √ TPR × TNR. Recall and G-mean were selected from a larger list of measures [11,34] mainly due to their complementary nature and easy interpretation. Recall focuses only on the minority class, allowing us to see when the recognition of the minority class drops.…”
Section: Experimental Aims and Setupmentioning
confidence: 99%
“…For example, if Recall improves but G-mean deteriorates, this means that the recognition of the minority class has improved at the cost of the recognition rate of the majority class. Moreover, G-mean is skew-invariant, meaning that G-mean's interpretation remains the same for all possible class imbalance ratios [11,12], being particularly relevant for studying drifting imbalance ratios.…”
Section: Experimental Aims and Setupmentioning
confidence: 99%
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“…Because of the accuracy as an evaluation, metrics will make sense only if the class labels of test data are uniformly distributed. So in this paper the performance of the various methods was evaluated based on the criteria as following: per-class precision, overall accuracy (OA), average recall, average F 1 -score and G-mean, which are considered to be easily interpretable and have better theoretical properties than other classification measures for class imbalanced problems [56]. Mutual usage of the G-mean measure and overall accuracy is considered in the evaluation of sampling schema in order to achieve the optimal performance for both majority and minority classes.…”
Section: Training and Metricsmentioning
confidence: 99%