  This paper presents the performance of a classifier built using the stackingC
algorithm in nine different data sets. Each data set is generated using a
sampling technique applied on the original imbalanced data set. Five new
sampling techniques are proposed in this paper (i.e., SMOTERandRep, Lax Random
Oversampling, Lax Random Undersampling, Combined-Lax Random Oversampling
Undersampling, and Combined-Lax Random Undersampling Oversampling) that were
based on the three sampling techniques (i.e., Random Undersampling, Random
Oversampling, and Synthetic Minority Oversampling Technique) usually used as
solutions in imbalance learning. The metrics used to evaluate the classifier's
performance were F-measure and G-mean. F-measure determines the performance of
the classifier for every class, while G-mean measures the overall performance
of the classifier. The results using F-measure showed that for the data without
a sampling technique, the classifier's performance is good only for the
majority class. It also showed that among the eight sampling techniques, RU and
LRU have the worst performance while other techniques (i.e., RO, C-LRUO and
C-LROU) performed well only on some classes. The best performing techniques in
all data sets were SMOTE, SMOTERandRep, and LRO having the lowest F-measure
values between 0.5 and 0.65. The results using G-mean showed that the
oversampling technique that attained the highest G-mean value is LRO (0.86),
next is C-LROU (0.85), then SMOTE (0.84) and finally is SMOTERandRep (0.83).
Combining the result of the two metrics (F-measure and G-mean), only the three
sampling techniques are considered as good performing (i.e., LRO, SMOTE, and
SMOTERandRep).
