title: A label distance maximum-based classifier for multi-label learning.
abstract: Multi-label classification is useful in many bioinformatics tasks such as gene function prediction and protein site localization. This paper presents an improved neural network algorithm, Max Label Distance Back Propagation Algorithm for Multi-Label Classification. The method was formulated by modifying the total error function of the standard BP by adding a penalty term, which was realized by maximizing the distance between the positive and negative labels. Extensive experiments were conducted to compare this method against state-of-the-art multi-label methods on three popular bioinformatic benchmark datasets. The results illustrated that this proposed method is more effective for bioinformatic multi-label classification compared to commonly used techniques.
authors.affiliation: Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.|College of Information Science and Engineering, Northeastern University, 110819, Shenyang, China.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Liu
authors.affiliation: Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.
authors.forename: Hang
authors.initials: H
authors.lastname: Bao
authors.affiliation: Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.|College of Information Science and Engineering, Northeastern University, 110819, Shenyang, China.
authors.forename: Dazhe
authors.initials: D
authors.lastname: Zhao
authors.affiliation: Medical Image Computing Laboratory of Ministry of Education, Northeastern University, 110819, Shenyang, China.|College of Information Science and Engineering, Northeastern University, 110819, Shenyang, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Cao
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D000069550:Machine Learning; D010363:Pattern Recognition, Automated / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods*; D020543:Proteome / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D020543:Proteome
keywords: Multi-label classification; max label distance; neural networks; self-adaptive learning rate