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 …
G Haixiang, L Yijing, J Shang, G Mingyun… - Expert systems with …, 2017 - Elsevier
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 …
CF Tsai, WC Lin, YH Hu, GT Yao - Information Sciences, 2019 - Elsevier
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 …
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 …
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 …
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 …
Most existing classification approaches assume the underlying training set is evenly distributed. In class imbalanced classification, the training set for one class (majority) far …
H Wang, Z Xu, H Fujita, S Liu - Information Sciences, 2016 - Elsevier
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 …