Dragi Kocev

3.6k citations
78 papers · 2.2k · 1 hit paper · h-index 22

Impact in

Papers in

Dragi Kocev

77 papers receiving 2.1k citations

Dragi Kocev's Hit Papers

An extensive experimental comparison of methods for multi-label learning 2012 · 497 citations
4970+4+9Years since publication100200300400

Peers

Dragi Kocev
Comparison fields: 5 of 172
  • Artificial Intelligence 1.1k
  • Computer Vision and Pattern Recognition 521
  • Media Technology 121
  • Information Systems 296
  • Ecological Modeling 45
Replace Bill Fulkerson with:
Bill Fulkerson United States
Tomás F. Pena Spain
K. R. K. Murthy Singapore
Liang Liu China
Francisco F. Rivera Spain
Jair Cervantes Mexico
Yuanyuan Chen China
Jiawei Luo China
Rujing Wang China
Xiaoli Z. Fern United States
Dragi Kocev relative to Bill Fulkerson United States Bill Fulkerson's profile →
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Countries citing papers authored by Dragi Kocev

Since Specialization
Citations

This map shows the geographic impact of Dragi Kocev'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 Dragi Kocev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dragi Kocev more than expected).

Fields of papers citing papers by Dragi Kocev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dragi Kocev. 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 Dragi Kocev. The network helps show where Dragi Kocev may publish in the future.

Co-authors

The 25 scholars most cited alongside Dragi Kocev, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Dragi Kocev Line = papers co-authored together Dragi Kocev links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 78 papers — load more, or switch the sort, to bring in the rest.

#Work
1
An extensive experimental comparison of methods for multi-label learning
Hit paper breakdown →
2012497
2 2012173
3 2009144
4 2010138
5 2020130
6 2011101
7 202361
8 201158
9 201449
10 201749
11 201845
12 201442
13 201141
14 201939
15 201836
16 201535
17 201731
18 201630
19 201530
20 200928

About Dragi Kocev

Dragi Kocev is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Information Systems and Media Technology, having authored 78 papers that have together received 2.2k indexed citations. Recurring topics across this work include Text and Document Classification Technologies (21 papers), Machine Learning and Data Classification (15 papers), Image Retrieval and Classification Techniques (10 papers), Machine Learning in Bioinformatics (9 papers), Remote-Sensing Image Classification (9 papers), Advanced Image and Video Retrieval Techniques (9 papers), Data Mining Algorithms and Applications (6 papers) and Face and Expression Recognition (5 papers). The work is most often cited by research in Artificial Intelligence (1.1k citations), Computer Vision and Pattern Recognition (521 citations), Media Technology (121 citations), Information Systems (296 citations) and Ecological Modeling (45 citations). Dragi Kocev has collaborated with scholars based in Slovenia, North Macedonia and Italy. Frequent co-authors include Sašo Džeroski, Gjorgji Madjarov, Dejan Gjorgjevikj, Ivica Dimitrovski, Jan Struyf, Celine Vens, Suzana Loškovska, Matej Petković, Jurica Levatić and Michelangelo Ceci. Their work appears in journals such as Machine Learning, Ecological Modelling, Information Sciences, Ecological Informatics and Pattern Recognition.

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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