title: hMuLab: A Biomedical Hybrid MUlti-LABel Classifier Based on Multiple Linear Regression.
abstract: Many biomedical classification problems are multi-label by nature, e.g., a gene involved in a variety of functions and a patient with multiple diseases. The majority of existing classification algorithms assumes each sample with only one class label, and the multi-label classification problem remains to be a challenge for biomedical researchers. This study proposes a novel multi-label learning algorithm, hMuLab, by integrating both feature-based and neighbor-based similarity scores. The multiple linear regression modeling techniques make hMuLab capable of producing multiple label assignments for a query sample. The comparison results over six commonly-used multi-label performance measurements suggest that hMuLab performs accurately and stably for the biomedical datasets, and may serve as a complement to the existing literature.
authors.forename: Pu
authors.initials: P
authors.lastname: Wang
authors.forename: Ruiquan
authors.initials: R
authors.lastname: Ge
authors.forename: Xuan
authors.initials: X
authors.lastname: Xiao
authors.forename: Manli
authors.initials: M
authors.lastname: Zhou
authors.forename: Fengfeng
authors.initials: F
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016014:Linear Models; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D012044:Regression Analysis