Random Bits Regression: a Strong General Predictor for Big Data by Yi Wang, Yi Li, Momiao Xiong, Li Jin

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 this https URL) is very fast and requires reasonable memories, therefore, provides a strong, robust and fast predictor in the big data era.

The implementation is here: http://sourceforge.net/projects/rbr/

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