Random bits regression: a strong general predictor for big data. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Wang, Yi; Li, Yi; Xiong, Momiao; Shugart, Yin; Jin, Li
• Is Part Of: Big data analytics. Volume 1:Issue 1(2016)
• Page Start: 1
• Page End: 10
• Publication Date: 2016-12
• Subjects: RBR -- Regression -- Classification -- Machine learning -- Big data prediction; Big data -- Periodicals; Biology -- Data processing -- Periodicals; 570.28557
• DOI: 10.1186/s41044-016-0010-4
• Languages: English
• ISSNs: 2058-6345

Abstract

Abstract Background Data-based modeling is becoming practical in predicting outcomes. In the era of big data, two practically conflicting challenges are eminent: (1) the prior knowledge on the subject is largely insufficient; (2) computation and storage cost of big data is unaffordable. Results 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. Conclusions RBR (available onhttps://sourceforge.net/projects/rbr/ ) is very fast and requires reasonable memories, therefore, provides a strong, robust and fast predictor in the big data era.