Andres Folleco

484 total citations
13 papers, 336 citations indexed

About

Andres Folleco is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Andres Folleco has authored 13 papers receiving a total of 336 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Information Systems and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Andres Folleco's work include Machine Learning and Data Classification (7 papers), Imbalanced Data Classification Techniques (6 papers) and Software Engineering Research (4 papers). Andres Folleco is often cited by papers focused on Machine Learning and Data Classification (7 papers), Imbalanced Data Classification Techniques (6 papers) and Software Engineering Research (4 papers). Andres Folleco collaborates with scholars based in United States and United Kingdom. Andres Folleco's co-authors include Taghi M. Khoshgoftaar, Jason Van Hulse, Chris Seiffert, Amri Napolitano, Ping An, Qiming Luo, Thomas J. Carlson and T.J. Tarn and has published in prestigious journals such as Information Sciences, IEEE Journal of Oceanic Engineering and Software Engineering and Knowledge Engineering.

In The Last Decade

Andres Folleco

13 papers receiving 318 citations

Peers

Andres Folleco

AI

IS

SOFTW

EEE

OE

Iyad Tumar Palestinian Territory

Ayaz Isazadeh Iran

Shanshan Wu China

Dejun Mu China

Zhonglin Liu China

Zhengjun Zhai China

Anikó Costa Portugal

Zhenkun Yang United States

Jun Song China

Iyad Tumar Palestinian Territory

Andres Folleco

88 ×0.5

AI

97 ×1.1

IS

52 ×0.8

SOFTW

87 ×1.4

EEE

58 ×1.1

OE

Citations per year, relative to Andres Folleco Andres Folleco (= 1×) peers Iyad Tumar

Countries citing papers authored by Andres Folleco

Since Specialization

Citations

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

Fields of papers citing papers by Andres Folleco

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andres Folleco

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

All Works

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13 of 13 papers shown

1.

Seiffert, Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, & Andres Folleco. (2011). An empirical study of the classification performance of learners on imbalanced and noisy software quality data. Information Sciences. 259. 571–595. 106 indexed citations

2.

Folleco, Andres, et al.. (2008). Analyzing the Impact of Attribute Noise on Software Quality Classification.. Software Engineering and Knowledge Engineering. 73–78. 2 indexed citations

3.

Folleco, Andres, et al.. (2008). Identifying learners robust to low quality data. 33(3). 190–195. 12 indexed citations

4.

Folleco, Andres, Taghi M. Khoshgoftaar, & Amri Napolitano. (2008). Comparison of Four Performance Metrics for Evaluating Sampling Techniques for Low Quality Class-Imbalanced Data. 153–158. 11 indexed citations

5.

Folleco, Andres, et al.. (2008). Software quality modeling: The impact of class noise on the random forest classifier. 3853–3859. 25 indexed citations

6.

Folleco, Andres, Taghi M. Khoshgoftaar, Jason Van Hulse, & Chris Seiffert. (2007). Learning from Software Quality Data with Class Imbalance and Noise.. Software Engineering and Knowledge Engineering. 82(2). 487–94. 2 indexed citations

7.

Khoshgoftaar, Taghi M., Chris Seiffert, Jason Van Hulse, Amri Napolitano, & Andres Folleco. (2007). Learning with limited minority class data. 348–353. 75 indexed citations

8.

Seiffert, Chris, Taghi M. Khoshgoftaar, Jason Van Hulse, & Andres Folleco. (2007). An Empirical Study of the Classification Performance of Learners on Imbalanced and Noisy Software Quality Data. 651–658. 21 indexed citations

9.

Khoshgoftaar, Taghi M., et al.. (2006). Software Quality Imputation in the Presence of Noisy Data. 484–489. 5 indexed citations

10.

Luo, Qiming, Taghi M. Khoshgoftaar, & Andres Folleco. (2006). Classification of Ships in Surveillance Video. 432–437. 13 indexed citations

11.

Carlson, Thomas J., et al.. (2004). Multiple communicating autonomous underwater vehicles. 4461–4464 Vol.5. 8 indexed citations

12.

An, Ping, et al.. (2003). Modeling and simulation of autonomous underwater vehicles: design and implementation. IEEE Journal of Oceanic Engineering. 28(2). 283–296. 44 indexed citations

13.

Folleco, Andres, et al.. (2002). High fidelity hardware-in-the-loop simulation development for an autonomous underwater vehicle. 1. 444–449. 12 indexed citations

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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