Identifying advisor-advisee relationships from co-author networks via a novel deep model

Zhongying Zhao, Wenqiang Liu, Yuhua Qian, Liqiang Nie, Yilong Yin, Yong Zhang

Advisor-advisee is one of the most important relationships in research publication networks. Identifying it can benefit many interesting applications, such as double-blind peer review, academic circle mining, and scientific community analysis.

Information Sciences, 2018, 466, 258-269.

A quantitative approach to reasoning about incomplete knowledge

Yanhong She, Xiaoli He, Yuhua Qian, Weihua Xu, Jinhai Li

In this paper, we aim to present a quantitative approach to reasoning about incomplete information. The study is conducted in MEL, a minimal epistemic logic relating modal languages to uncertainty theories.

Information Sciences, 2018, 451-452, 100-111.

Local rough set:a solution to rough data analysis in big data

Yuhua Qian, Xinyan Liang, Qi Wang, et al

As a supervised learning method, classical rough set theory often requires a large amount of labeled data, in which concept approximation and attribute reduction are two key issues.

International Journal of Approximate Reasoning,2018, 97，38-63.

Intuitionistic fuzzy rough set-based granular structures and attribute subset selection

Anhui Tan, Weizhi Wu, Yuhua Qian, Jiye Liang, Jinkun Chen, Jinjin Li

Attribute subset selection is an important issue in data mining and information processing. However, most automatic methodologies consider only the relevance factor between samples while ignoring the diversity factor.

IEEE Transactions on Fuzzy Systems, 2018, In Press.

Three novel accurate pixel-dreven projection methods for 2D CT and 3D EPR imaging

Zhiwei Qiao, Gage Relder, Zhiguo Gui, Yuhua Qian, Boris Epel, Howard Halpern

This work aims to explore more accurate pixel-driven projection methods for iterative image reconstructions in order to reduce high-frequency artifacts in the generated projection image.

Journal of X-Ray Science and Technology, 2018, 26(1), 83-102.

Feature selection based on neighborhood discrimination index

Changzhong Wang, Xizhao Wang, Degang Chen, Qinghua Hu, Yuhua Qian

Feature selection is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Neighborhood is one of the most important concepts in classification learning and can be

IEEE Transactions on Neural Networks and Learning Systems,2018, 29(7), 2986 - 2999.