The technology of drug delivery systems (DDSs) has demonstrated an outstanding
performance and effectiveness in production of pharmaceuticals, as it is proved by many …

Abstract The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well
known methods to solve the unequal class distribution problem in imbalanced datasets …

Imbalance in dataset enforces numerous challenges to implement data analytic in all
existing real world applications using machine learning. Data imbalance occurs when …

Given the importance that two-stage Data Envelopment Analysis (DEA) models have
attained in recent years, this paper presents a systematic review of the literature on the topic …

This document is a concise outline of some of the common mistakes that occur when using
machine learning, and what can be done to avoid them. Whilst it should be accessible to …

Data in real-world application often exhibit skewed class distribution which poses an intense
challenge for machine learning. Conventional classification algorithms are not effective in …

In recent years, class imbalance learning (CIL) has become an important branch of machine
learning. The Synthetic Minority Oversampling TEchnique (SMOTE) is considered to be a …

Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

Modern networks generate a massive amount of traffic data streams. Analyzing this data is
essential for various purposes, such as network resources management and cyber-security …

Many real-world applications reveal difficulties in learning classifiers from imbalanced data.
The rising big data era has been witnessing more classification tasks with large-scale but …