Andreas L. Prodromidis

2.0k total citations
15 papers, 1.2k citations indexed

About

Andreas L. Prodromidis is a scholar working on Information Systems, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Andreas L. Prodromidis has authored 15 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Information Systems, 10 papers in Artificial Intelligence and 3 papers in Computer Networks and Communications. Recurrent topics in Andreas L. Prodromidis's work include Data Mining Algorithms and Applications (9 papers), Imbalanced Data Classification Techniques (6 papers) and Data Stream Mining Techniques (5 papers). Andreas L. Prodromidis is often cited by papers focused on Data Mining Algorithms and Applications (9 papers), Imbalanced Data Classification Techniques (6 papers) and Data Stream Mining Techniques (5 papers). Andreas L. Prodromidis collaborates with scholars based in United States. Andreas L. Prodromidis's co-authors include Philip K. Chan, Salvatore J. Stolfo, Wei Fan, Wenke Lee, Sal Stolfo, Liana Fong, Mark S. Squillante and Nayeem Islam and has published in prestigious journals such as Knowledge and Information Systems, IEEE Intelligent Systems and their Applications and Knowledge Discovery and Data Mining.

In The Last Decade

Andreas L. Prodromidis

14 papers receiving 1.1k citations

Peers

Countries citing papers authored by Andreas L. Prodromidis

Since Specialization

Citations

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

Fields of papers citing papers by Andreas L. Prodromidis

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Andreas L. Prodromidis. 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 Andreas L. Prodromidis. The network helps show where Andreas L. Prodromidis may publish in the future.

Co-authorship network of co-authors of Andreas L. Prodromidis

This figure shows the co-authorship network connecting the top 25 collaborators of Andreas L. Prodromidis. A scholar is included among the top collaborators of Andreas L. Prodromidis 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 Andreas L. Prodromidis. Andreas L. Prodromidis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

#	Work	Indexed citations
1	Extensible resource management for cluster computing 2002 ·(unknown), Andreas L. Prodromidis, Mark S. Squillante, Liana Fong, (unknown)	3
2	Cost-based modeling for fraud and intrusion detection: results from the JAM project 2002 ·Columbia Academic Commons (Columbia University) ·Salvatore J. Stolfo, Wei Fan, Wenke Lee, Andreas L. Prodromidis, Philip K. Chan	377
3	Cost Complexity-Based Pruning of Ensemble Classifiers 2001 ·Knowledge and Information Systems ·Andreas L. Prodromidis, Salvatore J. Stolfo	55
4	Distributed Data Mining: The JAM system architecture 2001 ·Columbia Academic Commons (Columbia University) ·Andreas L. Prodromidis, Salvatore J. Stolfo, (unknown), (unknown), (unknown), (unknown)	3
5	Meta-Learning in Distributed Data Mining Systems: Issues and Approaches 2000 ·Andreas L. Prodromidis, Philip K. Chan, (unknown)	99
6	Minimal cost complexity pruning of meta-classifiers 1999 ·National Conference on Artificial Intelligence ·Andreas L. Prodromidis, Salvatore J. Stolfo	2
7	Agent-Based Distributed Learning Applied to Fraud Detection 1999 ·Columbia Academic Commons (Columbia University) ·Andreas L. Prodromidis, Salvatore J. Stolfo	30
8	Management of intelligent learning agents in distributed data mining systems 1999 ·Salvatore J. Stolfo, Andreas L. Prodromidis	5
9	Distributed data mining in credit card fraud detection 1999 ·IEEE Intelligent Systems and their Applications ·Philip K. Chan, Wei Fan, Andreas L. Prodromidis, Sal Stolfo	338
10	Mining databases with different schemas: integrating incompatible classifers 1998 ·Knowledge Discovery and Data Mining ·Andreas L. Prodromidis, Salvatore J. Stolfo	13
11	Pruning Classifiers in a Distributed Meta-Learning System 1998 ·Columbia Academic Commons (Columbia University) ·Andreas L. Prodromidis, Salvatore J. Stolfo	5
12	Extensible Resource Scheduling for Parallel Scientific Applications. 1997 ·PPSC ·Nayeem Islam, Andreas L. Prodromidis, Mark S. Squillante, (unknown), Liana Fong	2
13	JAM: java agents for meta-learning over distributed databases 1997 ·Knowledge Discovery and Data Mining ·Salvatore J. Stolfo, Andreas L. Prodromidis, (unknown), Wenke Lee, (unknown), Philip K. Chan	173
14	Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results 1 1997 ·Salvatore J. Stolfo, (unknown), Wenke Lee, Andreas L. Prodromidis, Philip K. Chan	125
15	On the management of distributed learning agents 1997 ·Columbia Academic Commons (Columbia University) ·Andreas L. Prodromidis	2

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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