The purpose of this study is to examine existing deep learning techniques for addressing
class imbalanced data. Effective classification with imbalanced data is an important area of …

Classifier learning with data-sets that suffer from imbalanced class distributions is a
challenging problem in data mining community. This issue occurs when the number of …

In this paper, a new stochastic optimizer, which is called slime mould algorithm (SMA), is
proposed based on the oscillation mode of slime mould in nature. The proposed SMA has …

Learning with imbalanced data refers to the scenario in which the amounts of instances that
represent the concepts in a given problem follow a different distribution. The main issue …

The interest in nonparametric statistical analysis has grown recently in the field of
computational intelligence. In many experimental studies, the lack of the required properties …

Data preprocessing is an often neglected but major step in the data mining process. The
data collection is usually a process loosely controlled, resulting in out of range values, eg …

This research's genesis is in two aspects: first, a guaranteed solution for mitigating the grey
wolf optimizer's (GWO) defect and deficiencies. Second, we provide new open-minding …

Abstract Linear Discriminant Analysis (LDA) assumes that all samples from the same class
are independently and identically distributed (iid). LDA may fail in the cases where the …

Training classifiers with datasets which suffer of imbalanced class distributions is an
important problem in data mining. This issue occurs when the number of examples …

Data Mining (DM) is the process for automatic discovery of high level knowledge by
obtaining information from real world, large and complex data sets [26], and is the core step …