hMuLab: A Biomedical Hybrid MUlti-LABel Classifier Based on Multiple Linear Regression
Pu Wang, Ruiquan Ge, Xuan Xiao, Manli Zhou, Fengfeng Zhou · IEEE Transactions on Computational Biology and Bioinformatics · 2016
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.