The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

Developing techniques for the machine learning of a classifier from class-imbalanced data
presents an important challenge. Among the existing methods for addressing this problem …

Due to the imbalanced distribution of business data, missing user features, and many other
reasons, directly using big data techniques on realistic business data tends to deviate from …

Computational synthesize of the metabolic pathway is take low cost while comparing with
the direct trial and error laboratory process. In real world data, more or less all datasets …

Abstract The Synthetic Minority Oversampling TEchnique (SMOTE) is widely-used for the
analysis of imbalanced datasets. It is known that SMOTE frequently over-generalizes the …

Since the last few decades, a class imbalance has been one of the most challenging
problems in various fields, such as data mining and machine learning. The particular state of …

Imbalanced data classification is one of the most widespread challenges in contemporary
pattern recognition. Varying levels of imbalance may be observed in most real datasets …

Imbalanced class data distribution occurs when the number of examples representing one
class is much lower than others. This conditioning affects the prediction accuracy degraded …

Abstract The Synthetic Minority Over Sampling TEchnique (SMOTE) is a widely used
technique to balance imbalanced data. In this paper we focus on improving SMOTE in the …