Correlation-based Feature Subset Selection for Machine Learning
- Authors
- Mark A. Hall
- Journal
- Medical Entomology and Zoology
In The Last Decade
doi.org/w71957622 →Countries where authors are citing Correlation-based Feature Subset Selection for Machine Learning
This map shows the geographic impact of Correlation-based Feature Subset Selection for Machine Learning. 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 Correlation-based Feature Subset Selection for Machine Learning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Correlation-based Feature Subset Selection for Machine Learning more than expected).
Fields of papers citing Correlation-based Feature Subset Selection for Machine Learning
This network shows the impact of Correlation-based Feature Subset Selection for Machine Learning. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Correlation-based Feature Subset Selection for Machine Learning.
About Correlation-based Feature Subset Selection for Machine Learning
This paper, published in 1998, received 694 indexed citations . Written by Mark A. Hall. It is primarily cited by scholars working on Artificial Intelligence (289 citations), Computer Vision and Pattern Recognition (124 citations) and Information Systems (115 citations). Published in Medical Entomology and Zoology.
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.
This paper is also available at doi.org/w71957622.