hMuLab: A Biomedical Hybrid MUlti-LABel Classifier Based on Multiple Linear Regression
hMuLab: A Biomedical Hybrid MUlti-LABel Classifier Based on Multiple Linear Regression
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DOI：
10.1109/tcbb.2016.2603507
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Pu Wang;Ruiquan Ge;Xuan Xiao;Manli Zhou;F. Zhou
中科院分区：
文献类型：
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作者：
Pu Wang;Ruiquan Ge;Xuan Xiao;Manli Zhou;F. Zhou
许多生物医学分类问题本质上是多标签的，例如，涉及多种功能的基因和患有多种疾病的患者。现有的大多数分类算法假定每个样本只有一个分类标签，多标签分类问题仍然是生物医学研究人员面临的一个挑战。通过综合基于特征和基于邻居的相似度得分，提出了一种新的多标签学习算法hMuLab。多元线性回归建模技术使hMuLab能够为查询样本产生多个标签分配。在六个常用的多标签性能测量上的比较结果表明，hMuLab对于生物医学数据集的性能准确和稳定，可以作为现有文献的补充。
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.