Random bits forest: A strong classifier/regressor for big data

Yi Wang, Yi Li, Weilin Pu, Kathryn Wen, Yin Yao Shugart, Momiao Xiong, Li Jin

Research output: Contribution to journalArticlepeer-review

Abstract

Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth), boosting (for width), and random forests (for prediction accuracy). Through a gradient boosting scheme, it first generates and selects ∼10,000 small, 3-layer random neural networks. These networks are then fed into a modified random forest algorithm to obtain predictions. Testing with datasets from the UCI (University of California, Irvine) Machine Learning Repository shows that RBF outperforms other popular methods in both accuracy and robustness, especially with large datasets (N > 1000). The algorithm also performed highly in testing with an independent data set, a real psoriasis genome-wide association study (GWAS).

Original languageEnglish (US)
Article number30086
JournalScientific Reports
Volume6
DOIs
StatePublished - Jul 22 2016
Externally publishedYes

ASJC Scopus subject areas

  • General

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