The recall measures the number of messages correctly classified penalised by the number of missed entries, whereas the precision measures the number of messages correctly classified penalised by the number of incorrect classifications [15]. In addition to the aforementioned three metrics, which are frequently used in studies, other metrics such as the F-score [15], geometric mean (G-mean) [15], false positive rates (FPR) [2], false negative rate (FNR) [2], receiver operating characteristics (ROC) [15], and loss [26] are also employed. The use of the different performance metrics is vital in the spam detection process due to the costs associated with misclassification. This misclassification is twofold, since it is concerned with identifying a spam as a ham or vice versa. Although identifying a spam as a ham is a matter of concern, it is usually not a critical issue because the only action the user has to take is to delete such message. However, in the case of misclassifying a ham message as a spam, the gravity of this situation can lead to chaos, since valuable information may be lost to the classification error of the underlying detection algorithm. Therefore, it is of utmost importance that a spam detection algorithm is tested and evaluated with multiple metrics. Continuing with the performance metrics, the aforementioned discussion as well as the review of literature in Section 2 point towards classifying the metrics into different levels. The first-level metrics are the ones that are used at the atomic level. These include true positive, true negative, false positive, and false negative. Then there are second-level metrics such as accuracy, precision, and recall, which are comprised of a certain combination of primary metrics. Finally, there are third-level metrics such as F-score and G-mean, which are based on secondary level metrics. Note that both F-score and G-mean combine two second-level metrics (F1 combines precision and recall, whereas G-mean combines specificity and recall). While there has been a great deal of effort in terms of developing new algorithms and datasets for the spam email detection problem, little emphasis has been placed on the evolution of new performance metrics, especially the third-level metrics. More specifically, while the first- and second-level metrics are frequently used in studies, as highlighted by the review of literature in Section 2, the impact of existing third-level metrics or the development of new metrics has received little attention from the research community. The main motivation behind the underlying study is to propose a metric that combines three second-level metrics--accuracy, recall, and precision--and therefore aggregate the effect of all three in one measure, rather than looking at the three individually. This aggregated approach gives convenience to the decision maker in reaching a conclusive decision. For example, consider a situation where we have three algorithms A, B, and C. Assume some hypothetical results as shown in Table 1 below:  
In the above scenario, A performs best in terms of Accuracy, B performs best in terms of Precision, and C performs best in terms of Recall, but which algorithm is the best in all respects? The answer is difficult and requires human intervention and expertise to reach a conclusion. In contrast, our proposed fuzzy measure aggregates the impact of all three measures, and therefore gives a single numerical value which can be easily and clearly evaluated without any confusion or human intervention. Furthermore, the commonly used measures of F1 score or G-mean would fail here, because they only aggregate the impact of only two second-level measures (as highlighted above). In this case, our proposed fuzzy metric is a suitable solution if it comes to measuring the combined effect of the three second-level metrics. Motivated by the aforementioned observations, the major and minor contributions of this study can be enumerated as follows:  
1. As a major contribution, a fuzzy-logic-based performance measure is proposed.