Mark A. Hall

7.8k total citations · 3 hit papers
15 papers, 4.8k citations indexed

About

Mark A. Hall is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Mark A. Hall has authored 15 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 7 papers in Information Systems and 6 papers in Computer Vision and Pattern Recognition. Recurrent topics in Mark A. Hall's work include Data Mining Algorithms and Applications (7 papers), Machine Learning and Data Classification (5 papers) and Face and Expression Recognition (4 papers). Mark A. Hall is often cited by papers focused on Data Mining Algorithms and Applications (7 papers), Machine Learning and Data Classification (5 papers) and Face and Expression Recognition (4 papers). Mark A. Hall collaborates with scholars based in New Zealand and Germany. Mark A. Hall's co-authors include Geoffrey Holmes, Lloyd A. Smith, Eibe Frank, Ian H. Witten, Sally Jo Cunningham, Leonard E. Trigg, Klaus Mayer, Hans‐Werner Mewes, Yu Wang and Igor V. Tetko and has published in prestigious journals such as The Journal of the Acoustical Society of America, IEEE Transactions on Knowledge and Data Engineering and Journal of Machine Learning Research.

In The Last Decade

Mark A. Hall

14 papers receiving 4.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning

2000 1.2k citations Mark A. Hall Research Commons (The University of Waikato) profile →
Benchmarking attribute selection techniques for discrete class data mining

2003 868 citations Mark A. Hall, Geoffrey Holmes IEEE Transactions on Knowledge and Data Engineering profile →
Correlation-based Feature Subset Selection for Machine Learning

1998 694 citations Mark A. Hall Medical Entomology and Zoology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Mark A. Hall New Zealand	12	2.3k	1.0k	999	915	428	15	4.8k
Marc K. Albert United States	14	3.1k 1.3×	1.1k 1.1×	1.0k 1.0×	455 0.5×	554 1.3×	26	5.4k
Dennis Kibler United States	21	3.8k 1.7×	1.3k 1.2×	1.0k 1.0×	612 0.7×	654 1.5×	55	6.4k
Charles X. Ling Canada	29	3.3k 1.4×	1.2k 1.1×	700 0.7×	552 0.6×	297 0.7×	131	5.7k
Markus Hagenbuchner Australia	17	3.1k 1.4×	820 0.8×	1.5k 1.5×	566 0.6×	339 0.8×	62	6.1k
Pádraig Cunningham Ireland	37	3.1k 1.3×	1.1k 1.1×	950 1.0×	550 0.6×	518 1.2×	205	5.9k
George H. John United States	10	4.2k 1.8×	1.2k 1.2×	2.0k 2.0×	1.2k 1.3×	732 1.7×	18	8.0k
Manoranjan Dash Singapore	20	3.4k 1.5×	1.1k 1.0×	1.5k 1.5×	793 0.9×	514 1.2×	68	5.8k
Jesse Davis Belgium	25	2.5k 1.1×	515 0.5×	798 0.8×	1.1k 1.2×	458 1.1×	151	5.9k
Edward H. Herskovits United States	26	3.1k 1.4×	633 0.6×	464 0.5×	813 0.9×	427 1.0×	92	6.6k

Countries citing papers authored by Mark A. Hall

Since Specialization

Citations

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

Fields of papers citing papers by Mark A. Hall

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark A. Hall. 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 Mark A. Hall. The network helps show where Mark A. Hall may publish in the future.

Co-authorship network of co-authors of Mark A. Hall

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

All Works

Sort: Min cites: Since: Top N: Style:

15 of 15 papers shown

Witten, Ian H., Eibe Frank, & Mark A. Hall. (2014). Data Mining: Practical Machine Learning Tools and Techniques, 3/E. 2 indexed citations

Bouckaert, Remco, Eibe Frank, Mark A. Hall, et al.. (2010). WEKA---Experiences with a Java Open-Source Project. Journal of Machine Learning Research. 11(87). 2533–2541. 190 indexed citations

Hall, Mark A. & Eibe Frank. (2008). Combining Naive Bayes and Decision Tables. Research Commons (University of Waikato). 318–319. 92 indexed citations

Wang, Yu, Igor V. Tetko, Mark A. Hall, et al.. (2005). Gene selection from microarray data for cancer classification—a machine learning approach. Computational Biology and Chemistry. 29(1). 37–46. 287 indexed citations

Hall, Mark A. & Geoffrey Holmes. (2003). Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering. 15(6). 1437–1447. 868 indexed citations breakdown →

Hall, Mark A.. (2002). Selection of attributes for modeling Bach chorales by a genetic algorithm. Research Commons (University of Waikato). 182–185. 1 indexed citations

Hall, Mark A.. (2000). Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning. Research Commons (The University of Waikato). 359–366. 1203 indexed citations breakdown →

Hall, Mark A. & Geoffrey Holmes. (2000). Benchmarking attribute selection techniques for data mining. Research Commons (University of Waikato). 25 indexed citations

Hall, Mark A. & Lloyd A. Smith. (1999). Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper. The Florida AI Research Society. 235–239. 389 indexed citations

10.

Hall, Mark A.. (1999). Feature selection for discrete and numeric class machine learning. Research Commons (University of Waikato). 56 indexed citations

11.

Witten, Ian H., Eibe Frank, Leonard E. Trigg, et al.. (1999). Weka: Practical machine learning tools and techniques with Java implementations. Research Commons (The University of Waikato). 421 indexed citations

12.

Hall, Mark A. & Lloyd A. Smith. (1998). Practical feature subset selection for machine learning. Research Commons (University of Waikato). 181–191. 357 indexed citations

13.

Hall, Mark A.. (1998). Correlation-based Feature Subset Selection for Machine Learning. Medical Entomology and Zoology. 694 indexed citations breakdown →

14.

Hall, Mark A. & Lloyd A. Smith. (1997). Feature subset selection: a correlation based filter approach. Research Commons (University of Waikato). 855–858. 185 indexed citations

15.

Hall, Mark A. & Lloyd A. Smith. (1996). A computer model of blues music and its evaluation. The Journal of the Acoustical Society of America. 100(2). 1163–1167. 8 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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