Heavy metal pollution poses a huge challenge to the soil environment. With the increasing
pollution level, the traditional monitoring methods cannot quickly obtain information on large …

Credit scoring is an effective tool for banks or lending institutions to identify potential bad
lenders and creditworthy applicants. Boosting ensemble approaches have made appealing …

For class-imbalance problems, traditional supervised learning algorithms tend to favor
majority instances (also called negative instances). Therefore, it is difficult for them to …

Imbalanced data classification is a challenging issue encountered in many practical
applications. Synthetic minority oversampling technique (SMOTE) and its variants are …

The class-imbalance problem is one of the researches of machine learning and data mining.
To address the class-imbalance problem, the traditional oversampling algorithm only utilizes …

An imbalanced classification problem is one in which the distribution of instances across
defined classes is uneven or biased in one direction or another. In data mining, the …

Learning a classifier from class-imbalance data is an important challenge. Among the
existing solutions, SMOTE has received great praise and features an extensive range of …

Imbalanced classification is widely referred in many real-world applications and has been
extensively studied. Most existing algorithms consider alleviating the imbalance by sampling …

Classification is a classical research field due to its broad applications in data mining such
as event extraction, spam detection, and medical treatment. However, class imbalance is an …

While existing imbalanced classification methods have made great progress, there are still
some challenges in the current imbalanced learning field:(1) How to achieve the balance …