Alex A. Freitas
About
In The Last Decade
Alex A. Freitas
224 papers receiving 7.6k citations
Hit Papers
Peers
Comparison fields: 5 of 209
- Artificial Intelligence 4.9k
- Information Systems 1.8k
- Molecular Biology 1.5k
- Computational Theory and Mathematics 1.3k
- Computer Vision and Pattern Recognition 739
Countries citing papers authored by Alex A. Freitas
This map shows the geographic impact of Alex A. Freitas'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 Alex A. Freitas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alex A. Freitas more than expected).
Fields of papers citing papers by Alex A. Freitas
This network shows the impact of papers produced by Alex A. Freitas. 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 Alex A. Freitas. The network helps show where Alex A. Freitas may publish in the future.
Co-authorship network of co-authors of Alex A. Freitas
This figure shows the co-authorship network connecting the top 25 collaborators of Alex A. Freitas. A scholar is included among the top collaborators of Alex A. Freitas 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 Alex A. Freitas. Alex A. Freitas is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature Spaces. | 2 |
| 2 | 0 | |
| 3 | Adapting random forests to cope with heavily censored datasets in survival analysis | 1 |
| 4 | A new genetic algorithm for multi-label correlation-based feature selection. | 9 |
| 5 | Exploring Attribute Selection in Hierarchical Classification | 5 |
| 6 | Improving Local Per Level Hierarchical Classification | 9 |
| 7 | A multi-label correlation-based feature selection method for the classification of neuroblastoma microarray data. | 8 |
| 8 | EDACluster: An Evolucionary Density and Grid-Based Clustering Algorithm. | 1 |
| 9 | 7 | |
| 10 | 77 | |
| 11 | Constructing X-of-n Attributes With A Genetic Algorithm | 5 |
| 12 | A genetic algorithm with sequential niching for discovering small-disjunct rules | 11 |
| 13 | An immunological algorithm for discovering small-disjunct rules in data mining | 10 |
| 14 | A genetic algorithm for the P-median problem | 36 |
| 15 | Incorporating deviation-detection functionality into the OLAP paradigm | 4 |
| 16 | Data Mining with Evolutionary Algorithms: Research Directions | 8 |
| 17 | A hybrid decision tree/genetic algorithm for coping with the problem of small disjuncts in data mining | 34 |
| 18 | Data Mining with Evolutionary Algorithms: Research Directions - Papers from the AAAI Workshop | 1 |
| 19 | A summary of the papers presented at the AAAI-99 and GECCO-99 Workshop on Data Mining with Evolutionary Algorithms: Research Directions | 0 |
| 20 | 5 |
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.