FN – number of false negatives.
False Negative (FN): Number of observations which are originally positive but classified as negative.
where tp is the number of true positives, tn number of true negatives, fp number of false positives and fn number of false negatives.
For each one of the variables studied in [ 24] (see Figure 3) we reconstructed the values of the true positive (TP), false positive (FP), true negative (TN) and false negative (FN) number of patients: TP = sensitivity × E FP = (sensitivity/LR) × NE TN = specificity × NE FN = N - (TP+FP+TN).
When we set T1 and T2 to 0.55 and 30 respectively, the foreground candidate pixels were about 5% of the entire number of pixels, and the Jaccard similarity of the proposed method was about 0.78 with Li's dataset and the FP and FN numbers were about 6,888 with Wallflower's dataset.
False negative (FN) = number of sensitisers predicted as non sensitisers.
Fig. 6 Detection accuracy and FN versus number of features.
where TP, TN, FP, and FN denote number of true positives, true negatives, false positives, and false negatives, respectively.
Table 1 Fibonacci order, substructure, and sequence FN substructure FN sequence and number Generators: of defects Generators: Order F0= C, F1= CD F0= 0, F1= 1 2 CDC 1 3 CDCCD 1 4 CDCCDCDC 2 5 CDCCDCDCCDCCD 3 6 CDCCDCDCCDCCDCDCCDCDC 5 7 CDCCDCDCCDCCDCDCCDCDC… 8 FN, Fibonacci.
The prediction quality was assessed by the recall and the precision, which are, respectively, defined as: (17) (18) where TP, FP and FN are numbers of true positive predictions, false positive predictions and false negative predictions, respectively.
