Liangxiao Jiang

5.1k citations
114 papers · 3.6k indexed · 1 hit paper · h-index 31
Topics
Bayesian Modeling and Causal Inference (41 papers)Machine Learning and Data Classification (41 papers)Imbalanced Data Classification Techniques (40 papers)
Partner nations
ChinaCanadaAustralia

In The Last Decade

Liangxiao Jiang

108 papers receiving 3.4k citations

Hit Papers

Deep feature weighting for naive Bayes and its applicatio...2016202620192022201650100150200250

Peers

Liangxiao Jiang
Comparison fields: 5 of 158
  • Artificial Intelligence 2.6k
  • Information Systems 772
  • Computer Vision and Pattern Recognition 548
  • Computational Theory and Mathematics 456
  • Computer Science Applications 383
Replace Alberto Cano with:
Alberto Cano United States
María José del Jesús Spain
Chaoqun Li China
Petra Perner Germany
Kalyan Veeramachaneni United States
Joshua Zhexue Huang China
Jesús Alcalá‐Fdez Spain
Ivan Bratko Slovenia
Isaac Triguero Spain
Zhikui Chen China
Liangxiao Jiang relative to Alberto Cano United States Alberto Cano's profile →
Citations per field
00.5×2.6×
Alberto Cano · 1×
Citations per year

Countries citing papers authored by Liangxiao Jiang

Since Specialization
Citations

This map shows the geographic impact of Liangxiao Jiang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Liangxiao Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liangxiao Jiang more than expected).

Fields of papers citing papers by Liangxiao Jiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Liangxiao Jiang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Liangxiao Jiang. The network helps show where Liangxiao Jiang may publish in the future.

Co-authorship network of co-authors of Liangxiao Jiang

This figure shows the co-authorship network connecting the top 25 collaborators of Liangxiao Jiang. A scholar is included among the top collaborators of Liangxiao Jiang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Liangxiao Jiang. Liangxiao Jiang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 0
3 0
4 4
5 11
6 2
7 0
8 2
9 1
10 2
11 6
12 8
13 7
14 209
15 7
16 1
17 24
18 31
19 0
20
Hidden naive Bayes
59

About Liangxiao Jiang

Liangxiao Jiang is a scholar working on Computer Science Applications, Artificial Intelligence and Computational Theory and Mathematics, having authored 114 papers that have together received 3.6k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (41 papers), Machine Learning and Data Classification (41 papers) and Imbalanced Data Classification Techniques (40 papers). The work is most often cited by research in Artificial Intelligence (2.6k citations), Computer Science Applications (383 citations) and Information Systems (772 citations). Liangxiao Jiang has collaborated with scholars based in China, Canada and Australia. Frequent co-authors include Chaoqun Li, Zhihua Cai, Lungan Zhang, Dianhong Wang, Huan Zhang, Liangjun Yu, H. Zhang, Shasha Wang, Harry Zhang and Jia Wu. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Geoscience and Remote Sensing and Expert Systems with Applications.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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