Random Bits Regression: a Strong General Predictor for Big Data
Abstract
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.
Cite
@article{arxiv.1501.02990,
title = {Random Bits Regression: a Strong General Predictor for Big Data},
author = {Yi Wang and Yi Li and Momiao Xiong and Li Jin},
journal= {arXiv preprint arXiv:1501.02990},
year = {2016}
}
Comments
20 pages,1 figure, 2 tables, research article