Class imbalance is a long-standing problem relevant to a number of real-world applications
of deep learning. Oversampling techniques, which are effective for handling class imbalance …

One key challenging problem in data mining and decision-making is to establish a decision
support system based on unbalanced datasets. In this study, we propose a novel algorithm …

Any computer vision application development starts off by acquiring images and data, then
preprocessing and pattern recognition steps to perform a task. When the acquired images …

In the field of artificial intelligence, classification algorithms tend to be biased toward the
majority class samples when encountering imbalanced data, resulting in low recognition …

Multi-class Classification of Imbalanced Intelligent Data using Deep Neural Network Page 1 EAI
Endorsed Transactions on AI and Robotics Research Article Multi-class Classification of …

Increasing the number of minority samples by data generation can effectively improve the
performance of mining minority samples using a classifier in imbalanced problems. In this …

Many machine learning problem domains, such as the detection of fraud, spam, outliers, and
anomalies, tend to involve inherently imbalanced class distributions of samples. However …

Nowadays, the solution to many practical problems relies on machine learning tools.
However, compiling the appropriate training data set for real-world classification problems is …

Extreme learning machine (ELM) for single-hidden-layer feedforward neural networks
(SLFN) is a powerful machine learning technique, and has been attracting attentions for its …

Imbalanced datasets are pervasive in classification tasks and would cause degradation of
the performance of classifiers in predicting minority samples. Oversampling is effective in …