title: A novel hierarchical selective ensemble classifier with bioinformatics application.
abstract: Selective ensemble learning is a technique that selects a subset of diverse and accurate basic models in order to generate stronger generalization ability. In this paper, we proposed a novel learning algorithm that is based on parallel optimization and hierarchical selection (PTHS). Our novel feature selection method is based on maximize the sum of relevance and distance (MSRD) for solving the problem of high dimensionality. Specifically, we have a PTHS algorithm that employs parallel optimization and candidate model pruning based on k-means and a hierarchical selection framework. We combine the prediction result of each basic model by majority voting, which employs the divide-and-conquer strategy to save computing time. In addition, the PT algorithm is capable to transform a multi-class problem into a binary classification problem, and thereby allowing our ensemble model to address multi-class problems. Empirical study shows that MSRD is efficient in solving the high dimensionality problem, and PTHS exhibits better performance than the other existing classification algorithms. Most importantly, our classifier achieved high-level performance on several bioinformatics problems (e.g. tRNA identification, and protein-protein interaction prediction, etc.), demonstrating efficiency and robustness.
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin, China.
authors.forename: Leyi
authors.initials: L
authors.lastname: Wei
authors.affiliation: School of Computer Science and Technology, Tianjin University, Tianjin, China.
authors.forename: Shixiang
authors.initials: S
authors.lastname: Wan
authors.affiliation: School of Information Science and Technology, Xiamen University, Xiamen, China.
authors.forename: Jiasheng
authors.initials: J
authors.lastname: Guo
authors.affiliation: School of Medicine, Western Sydney University, Sydney, Australia. Electronic address: Kelvin.Wong@westernsydney.edu.au.
authors.forename: Kelvin Kl
authors.initials: KK
authors.lastname: Wong
mesh_terms: D019540:Area Under Curve; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining / Q000379:methods*; D030541:Databases, Genetic; D000069550:Machine Learning; D060066:Protein Interaction Maps; D011506:Proteins / Q000145:classification* / Q000378:metabolism; D012343:RNA, Transfer / Q000145:classification* / Q000235:genetics / Q000378:metabolism; D012372:ROC Curve; D015203:Reproducibility of Results
chemical_list: D011506:Proteins; D012343:RNA, Transfer
keywords: Bioinformatics; Divide and conquer; Multi-class classification; Parallel optimization; Selective ensemble learning