In machine learning, the data imbalance imposes challenges to perform data analytics in
almost all areas of real-world research. The raw primary data often suffers from the skewed …

Big data analytics has gained wide attention from both academia and industry as the
demand for understanding trends in massive datasets increases. Recent developments in …

In a majority–minority classification problem, class imbalance in the dataset (s) can
dramatically skew the performance of classifiers, introducing a prediction bias for the …

Rare events, especially those that could potentially negatively impact society, often require
humans' decision-making responses. Detecting rare events can be viewed as a prediction …

Class imbalanced datasets are common across different domains including health, security,
banking and others. A typical supervised learning algorithm tends to be biased towards the …

Class-imbalanced datasets, ie, those with the number of data samples in one class being
much larger than that in another class, occur in many real-world problems. Using these …

Online reviews are often the primary factor in a customer's decision to purchase a product or
service, and are a valuable source of information that can be used to determine public …

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

Abstract The era of Big Data has arrived along with large volume, complex and growing data
generated by many distinct sources. Nowadays, nearly every aspect of the modern society is …

In this paper, we compute the sentiment of social media posts using a novel set of fuzzy
rules involving multiple lexicons and datasets. The proposed fuzzy system integrates Natural …