title: Random Bits Forest: a Strong Classifier/Regressor for Big Data.
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).
authors.affiliation: Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200433, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200433, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Li
authors.affiliation: Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200433, China.
authors.forename: Weilin
authors.initials: W
authors.lastname: Pu
authors.affiliation: Unit on Statistical Genomics, Division of Intramural Division Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
authors.forename: Kathryn
authors.initials: K
authors.lastname: Wen
authors.affiliation: Unit on Statistical Genomics, Division of Intramural Division Programs, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
authors.forename: Yin Yao
authors.initials: YY
authors.lastname: Shugart
authors.affiliation: Human Genetics Center, School of Public Health, University of Texas Houston Health Sciences Center, Houston, Texas, USA.
authors.forename: Momiao
authors.initials: M
authors.lastname: Xiong
authors.affiliation: Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai 200433, China.
authors.forename: Li
authors.initials: L
authors.lastname: Jin
mesh_terms: D056808:Biostatistics / Q000379:methods*; D055106:Genome-Wide Association Study; D006801:Humans; D000069550:Machine Learning; D016571:Neural Networks, Computer; D011565:Psoriasis / Q000235:genetics; D013223:Statistics as Topic / Q000379:methods*