J
Jiuyong Li
Researcher at University of South Australia
Publications - 335
Citations - 6808
Jiuyong Li is an academic researcher from University of South Australia. The author has contributed to research in topics: Computer science & Association rule learning. The author has an hindex of 38, co-authored 285 publications receiving 5280 citations. Previous affiliations of Jiuyong Li include Kunming University of Science and Technology & Griffith University.
Papers
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Proceedings ArticleDOI
Evidence Weighted Tree Ensembles for Text Classification
TL;DR: The presented method weights predictions based on word presence so that it outperforms state-of-the-art ensemble methods on various text classification tasks.
Ancestral instrument method for causal inference without a causal graph
Debo Cheng,Jiuyong Li,Lin Liu,Jiji Zhang,Thuc Duy Le,Jixue Liu Stem,U. O. S. Australia,Adelaide,Sa,Australia,Department of Religion,Philosophy,Hong Kong Baptist University,Hong Kong,China +14 more
TL;DR: By leveraging maximal ancestral graphs (MAGs) in causal inference with latent variables, a new type of IV is proposed, ancestral IV in MAG, and the theory to support data-driven discovery of the conditioning set for a given ancestralIV in MAG is developed.
Journal ArticleDOI
SiO2: A Novel Electrolyte for High-Performance All-Solid-State Electrochromic Devices
TL;DR: In this article , the amorphous SiO2 film prepared by pulsed direct current reactive magnetron sputtering was used as the electrolyte layer in the ECD for the first time and shows promising potential due to its ultrahigh transparency, good intrinsic electronic insulation, and loose structure for fast ion conduction.
Proceedings ArticleDOI
A simple yet effective data integration approach to tree-based microarray data classification
TL;DR: This paper proposes a method for integrating microarray data from multiple sources for building classification models and demonstrates that it is possible to build consistent models using data sets from multiple labs.
Proceedings ArticleDOI
Disentangled Representation with Causal Constraints for Counterfactual Fairness
TL;DR: This work theoretically demonstrates that using the structured representations enable downstream predictive models to achieve counterfactual fairness, and proposes the Counterfactual Fairness Variational AutoEncoder (CF-VAE) to obtain structured representations with respect to domain knowledge.