David W. Opitz

4.7k total citations · 1 hit paper
23 papers, 3.1k citations indexed

About

David W. Opitz is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computational Theory and Mathematics. According to data from OpenAlex, David W. Opitz has authored 23 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Computational Theory and Mathematics. Recurrent topics in David W. Opitz's work include Neural Networks and Applications (12 papers), Machine Learning and Data Classification (7 papers) and Evolutionary Algorithms and Applications (4 papers). David W. Opitz is often cited by papers focused on Neural Networks and Applications (12 papers), Machine Learning and Data Classification (7 papers) and Evolutionary Algorithms and Applications (4 papers). David W. Opitz collaborates with scholars based in United States and Germany. David W. Opitz's co-authors include Richard Maclin, Jude Shavlik, C. Kenneth Brewer, Joanne Winne, Roland L. Redmond, Subhash C. Basak, Brian D. Gute, Gregory D. Grunwald, K. Balasubramanian and HH Bülthoff and has published in prestigious journals such as Photogrammetric Engineering & Remote Sensing, Journal of Artificial Intelligence Research and Connection Science.

In The Last Decade

David W. Opitz

23 papers receiving 2.9k 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.

Popular Ensemble Methods: An Empirical Study

1999 1.9k citations David W. Opitz, Richard Maclin Journal of Artificial Intelligence Research profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
David W. Opitz United States	10	1.6k	592	279	234	220	23	3.1k
Senén Barro Spain	30	1.6k 1.0×	444 0.8×	370 1.3×	197 0.8×	285 1.3×	137	4.4k
Juha Vesanto Finland	13	1.5k 0.9×	653 1.1×	234 0.8×	201 0.9×	123 0.6×	18	3.7k
S.R. Gunn United Kingdom	16	1.3k 0.8×	1.1k 1.9×	231 0.8×	352 1.5×	243 1.1×	41	4.4k
Juan J. Rodríguez Spain	25	1.8k 1.1×	699 1.2×	330 1.2×	282 1.2×	170 0.8×	76	3.3k
Esa Alhoniemi Finland	10	1.1k 0.7×	479 0.8×	199 0.7×	162 0.7×	78 0.4×	27	3.0k
Bill Fulkerson United States	9	1.2k 0.7×	443 0.7×	283 1.0×	168 0.7×	163 0.7×	15	2.5k
William E. Full United States	10	2.1k 1.2×	1.3k 2.2×	389 1.4×	203 0.9×	228 1.0×	25	5.0k
S.R. Safavian United States	4	1.0k 0.6×	357 0.6×	394 1.4×	197 0.8×	150 0.7×	11	2.7k
Richard Maclin United States	13	1.6k 0.9×	519 0.9×	252 0.9×	215 0.9×	145 0.7×	29	2.7k
Jie Sun China	36	1.1k 0.7×	358 0.6×	159 0.6×	246 1.1×	250 1.1×	186	4.3k

Countries citing papers authored by David W. Opitz

Since Specialization

Citations

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

Fields of papers citing papers by David W. Opitz

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David W. Opitz. 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 David W. Opitz. The network helps show where David W. Opitz may publish in the future.

Co-authorship network of co-authors of David W. Opitz

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

All Works

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20 of 20 papers shown

1.

Opitz, David W., et al.. (2008). AN APPROACH FOR COLLECTION OF GEOSPECIFIC 3D FEATURES FROM TERRESTRIAL LIDAR. 3 indexed citations

2.

Opitz, David W., et al.. (2006). GROUND SURFACE EXTRACTION FROM SIDE-SCAN (VEHICULAR) LIDAR. 5 indexed citations

3.

Brewer, C. Kenneth, et al.. (2005). Classifying and Mapping Wildfire Severity. Photogrammetric Engineering & Remote Sensing. 71(11). 1311–1320. 155 indexed citations

4.

Opitz, David W.. (2002). Analyzing the structure of a neural network using principal component analysis. Proceedings of International Conference on Neural Networks (ICNN'97). 1. 254–259. 1 indexed citations

5.

Opitz, David W., et al.. (2001). Feature Extraction from Digital Imagery: A Hierarchical Method.. 283–288. 2 indexed citations

6.

Basak, Subhash C., Gregory D. Grunwald, Brian D. Gute, K. Balasubramanian, & David W. Opitz. (2000). Use of Statistical and Neural Net Approaches in Predicting Toxicity of Chemicals. Journal of Chemical Information and Computer Sciences. 40(4). 885–890. 66 indexed citations

7.

Basak, Subhash C., Gregory D. Grunwald, Brian D. Gute, K. Balasubramanian, & David W. Opitz. (2000). ChemInform Abstract: Use of Statistical and Neutral Net Approaches in Predicting Toxicity of Chemicals.. ChemInform. 31(41). 1 indexed citations

8.

Opitz, David W., Subhash C. Basak, & Brian D. Gute. (1999). Hazard assessment modeling: an evolutionary ensemble approach. Genetic and Evolutionary Computation Conference. 1643–1650. 7 indexed citations

9.

Opitz, David W.. (1999). An evolutionary approach to feature set selection. Genetic and Evolutionary Computation Conference. 803–803. 1 indexed citations

10.

Opitz, David W.. (1999). Feature selection for ensembles. National Conference on Artificial Intelligence. 379–384. 200 indexed citations

11.

Basak, Subhash C., Brian D. Gute, Gregory D. Grunwald, David W. Opitz, & K. Balasubramanian. (1999). Use of Statistical and Neural Net Methods in Predicting Toxicity of Chemicals: A Hierarchical QSAR Approach. 2 indexed citations

12.

Opitz, David W. & Jude Shavlik. (1999). A Genetic Algorithm Approach for Creating Neural-Network Ensembles. 8 indexed citations

13.

Opitz, David W. & Richard Maclin. (1999). Popular Ensemble Methods: An Empirical Study. Journal of Artificial Intelligence Research. 11. 169–198. 1947 indexed citations breakdown →

14.

Opitz, David W.. (1997). The Effective Size of a Neural Network: A Principal Component Approach. International Conference on Machine Learning. 263–271. 1 indexed citations

15.

Maclin, Richard & David W. Opitz. (1997). An empirical evaluation of bagging and boosting. National Conference on Artificial Intelligence. 546–551. 176 indexed citations

16.

Opitz, David W. & Jude Shavlik. (1997). Connectionist Theory Refinement: Genetically Searching the Space of Network Topologies. Journal of Artificial Intelligence Research. 6. 177–209. 35 indexed citations

17.

Opitz, David W. & Jude Shavlik. (1996). Actively Searching for an Effective Neural Network Ensemble. Connection Science. 8(3-4). 337–354. 215 indexed citations

18.

Opitz, David W. & Jude Shavlik. (1995). Generating Accurate and Diverse Members of a Neural-Network Ensemble. Neural Information Processing Systems. 8. 535–541. 190 indexed citations

19.

Opitz, David W. & Jude Shavlik. (1993). Heuristically Expanding Knowledge-Based Neural Networks. 13 indexed citations

20.

Opitz, David W., et al.. (1993). Optimal grasp points: computational theory and human psychophysics. MPG.PuRe (Max Planck Society). 123. 5 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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