Andrea Capotorti

29 papers receiving 271 citations

Peers

Andrea Capotorti
Comparison fields: 5 of 74
  • Artificial Intelligence 171
  • Computational Theory and Mathematics 66
  • Management Science and Operations Research 64
  • Economics and Econometrics 34
  • Finance 32
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Countries citing papers authored by Andrea Capotorti

Since Specialization
Citations

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

Fields of papers citing papers by Andrea Capotorti

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andrea Capotorti

This figure shows the co-authorship network connecting the top 25 collaborators of Andrea Capotorti. A scholar is included among the top collaborators of Andrea Capotorti 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 Andrea Capotorti. Andrea Capotorti 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 1
2 5
3 5
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Naive Bayesian classifiers with extreme probability features
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5 10
6 14
7 17
8 16
9
On the use of a new discrepancy measure to correct incoherent assessments and to aggregate conflicting opinions based on imprecise conditional probabilities
3
10 0
11 4
12
Configurations of Locally Strong Coherence in the Presence of Conditional Exchangeability (the case of cardinality k <= 3).
0
13 14
14 31
15
A simplified algorithm for inference by lower conditional probabilities.
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16 3
17
Local coherence of conditional probability assessments: definition and application
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18
NON ADDITIVE ORDINAL RELATIONS REPRESENTABLE BY LOWER OR UPPER PROBABILITIES
8
19
A Comparison Between Classical Logic and Three Valued logic for Conditional Events
4
20
The Consistency Problem in Belief and Probability Assessments
4

About Andrea Capotorti

Andrea Capotorti is a scholar working on General Decision Sciences, Artificial Intelligence and Computational Theory and Mathematics, having authored 34 papers that have together received 295 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (17 papers), Logic, Reasoning, and Knowledge (13 papers) and Rough Sets and Fuzzy Logic (6 papers). The work is most often cited by research in General Decision Sciences (15 citations), Artificial Intelligence (171 citations) and Management Science and Operations Research (64 citations). Andrea Capotorti has collaborated with scholars based in Italy, Netherlands and United States. Frequent co-authors include Barbara Vantaggi, Marco Baioletti, Gianna Figà‐Talamanca, Giulianella Coletti, Alessandro Brozzi, Laura Galli, Andrea Formisano, Raffaella Branciari, Linda C. van der Gaag and Roberta Ortenzi. Their work appears in journals such as Fuzzy Sets and Systems, Food Control and Computational Statistics & Data Analysis.

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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