Nature

A cluster-based SMOTE both-sampling (CSBBoost) ensemble algorithm for classifying imbalanced data

In this paper, a Cluster-based Synthetic minority oversampling technique (SMOTE) Both-sampling (CSBBoost) ensemble algorithm is proposed for classifying imbalanced data. In this algorithm, a ...
Nature

Research and performance analysis of random forest-based feature selection algorithm in sports effectiveness evaluation

The rapid progress in fields such as data mining and machine learning, as well as the explosive growth of sports big data, have posed new challenges to the research of sports big data. Most of the ...
Investopedia

How Stratified Random Sampling Works, With Examples

Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...
Investopedia

Simple vs. Stratified Random Sampling: Key Differences Explained

Mary Hall is a editor for Investopedia's Advisor Insights, in addition to being the editor of several books and doctoral papers. Mary received her bachelor's in English from Kent State University with ...
The Conversation

How random is your randomness, and why does it matter?

Randomness is powerful. Think about a presidential poll: A random sample of just 400 people in the United States can accurately estimate Clinton’s and Trump’s support to within 5 percent (with 95 ...