Fei Zheng 0001

Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.

? Tutorial for this page

Demo Data from SIGIR 2026 / reviewers in the wild / expert

dblp:181/2782 · DBLP profile ↗

← Back

5ranked-venue papers

3as first author

0since 2021 · last 2012

—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 2 first-authorDatabases, data management, data science and information retrieval · 2 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence

1 paper

Probabilistic and Bayesian machine learning · 67% Knowledge representation and reasoning · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning attribute reduction	0.1	1	2006	Efficient lazy elimination for averaged one-dependence estimators · ICML 2006
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference bayesian classification	0.1	1	2006	Efficient lazy elimination for averaged one-dependence estimators · ICML 2006
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference › bayesian classification semi-naive bayes	0.1	1	2006	Efficient lazy elimination for averaged one-dependence estimators · ICML 2006

Methods — techniques the papers use, named apart from their topics

backwards sequential elimination · 0.1

Year	Publication	Venue	Position
2012	Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive Bayesian classification Geoffrey I. Webb, Janice R. Boughton, Fei Zheng 0001, Kai Ming Ting, Houssam Salem	Mach. Learn.	3
2012	Subsumption resolution: an efficient and effective technique for semi-naive Bayesian learning Fei Zheng 0001, Geoffrey I. Webb, Pramuditha Suraweera, Liguang Zhu	Mach. Learn.	1
2007	Finding the Right Family: Parent and Child Selection for Averaged One-Dependence Estimators Fei Zheng 0001, Geoffrey I. Webb	ECML	1
2006	Efficient lazy elimination for averaged one-dependence estimators abstract Semi-naive Bayesian classifiers seek to retain the numerous strengths of naive Bayes while reducing error by relaxing the attribute independence assumption. Backwards Sequential Elimination (BSE) is a wrapper technique for attribute elimination that has proved effective at this task. We explore a new technique, Lazy Elimination (LE), which eliminates highly related attribute-values at classification time without the computational overheads inherent in wrapper techniques. We analyze the effect of LE and BSE on a state-of-the-art semi-naive Bayesian algorithm Averaged One-Dependence Estimators (AODE). Our experiments show that LE significantly reduces bias and error without undue computation, while BSE significantly reduces bias but not error, with high training time complexity. In the context of AODE, LE has a significant advantage over BSE in both computational efficiency and error. 1. Fei Zheng 0001, Geoffrey I. Webb	ICML	1
2004	Selective Augmented Bayesian Network Classifiers Based on Rough Set Theory Geoffrey I. Webb, Fei Zheng 0001	PAKDD	3