  To improve accuracy and speed of regressions and classifications, we present
a data-based prediction method, Random Bits Regression (RBR). This method first
generates a large number of random binary intermediate/derived features based
on the original input matrix, and then performs regularized linear/logistic
regression on those intermediate/derived features to predict the outcome.
Benchmark analyses on a simulated dataset, UCI machine learning repository
datasets and a GWAS dataset showed that RBR outperforms other popular methods
in accuracy and robustness. RBR (available on
https://sourceforge.net/projects/rbr/) is very fast and requires reasonable
memories, therefore, provides a strong, robust and fast predictor in the big
data era.

Machine Learning;Machine Learning