T
Tu Bao Ho
Researcher at Japan Advanced Institute of Science and Technology
Publications - 214
Citations - 3145
Tu Bao Ho is an academic researcher from Japan Advanced Institute of Science and Technology. The author has contributed to research in topics: Knowledge extraction & Rough set. The author has an hindex of 27, co-authored 214 publications receiving 2694 citations. Previous affiliations of Tu Bao Ho include Vietnam Academy of Science and Technology & University of Engineering and Technology, Lahore.
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Journal ArticleDOI
Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View.
Wei Luo,Dinh Phung,Truyen Tran,Sunil Gupta,Santu Rana,Chandan Karmakar,Alistair Shilton,John Yearwood,Nevenka Dimitrova,Tu Bao Ho,Svetha Venkatesh,Michael Berk +11 more
TL;DR: A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research and it is believed that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community.
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Multiple-attribute decision making under uncertainty: the evidential reasoning approach revisited
TL;DR: This paper reanalyzes the ER approach explicitly in terms of D-S theory and then proposes a general scheme of attribute aggregation in MADM under uncertainty and shows that new aggregation schemes also satisfy the synthesis axioms.
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Nonhierarchical Document Clustering Based on a Tolerance Rough Set Model
Tu Bao Ho,Ngoc Binh Nguyen +1 more
TL;DR: This article introduces a nonhierarchical document clustering algorithm based on a proposed tolerance rough set model (TRSM) that can be applied to large document databases and can be well adapted to documents characterized by a few terms due to the TRSM's ability of semantic calculation.
Journal Article
Unsupervised Feature Extraction for Time Series Clustering Using Orthogonal Wavelet Transform
TL;DR: Unsupervised Feature Extraction for Time Series Clustering Using Orthogonal Wavelet Transform using Orthogonic Wavelets Transform.
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Finding microRNA regulatory modules in human genome using rule induction
TL;DR: A rule-based learning method is proposed to identify groups of miRNAs and target genes that are believed to participate cooperatively in the post-transcriptional gene regulation, so-called miRNA regulatory modules (MRMs).