The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

Classification of imbalanced data is a vastly explored issue of the last and present decade
and still keeps the same importance because data are an essential term today and it …

Abstract The Matthews Correlation Coefficient (MCC) is one of the popular measurements
for classification accuracy. It has been generally regarded as a balanced measure which …

Most existing classification approaches assume the underlying training set is evenly
distributed. In class imbalanced classification, the training set for one class (majority) far …

The class imbalance problems have been reported to severely hinder classification
performance of many standard learning algorithms, and have attracted a great deal of …

Imbalanced data learning has become a major challenge in data mining and machine
learning. Oversampling is an effective way to re-achieve the balance by generating new …

Data plays a key role in the design of expert and intelligent systems and therefore, data
preprocessing appears to be a critical step to produce high-quality data and build accurate …

In this paper, we propose a new random forest (RF) based ensemble method, F ores T exter,
to solve the imbalanced text categorization problems. RF has shown great success in many …

Big Data and Big Data Analytics has gained huge prominence over the recent years. Their
ability to handle massive amounts of data at ease has made them the preferred choice for …

Nontuberculous mycobacteria (NTM) infection diagnosis remains a challenge due to its
overlap** clinical symptoms with tuberculosis (TB), leading to inappropriate treatment …