| Genetic algorithms in feature and instance selection CF Tsai, W Eberle, CY Chu Knowledge-Based Systems 39, 240-247, 2013 | 274 | 2013 |
| Insider threat detection using a graph-based approach W Eberle, J Graves, L Holder Journal of Applied Security Research 6 (1), 32-81, 2010 | 193 | 2010 |
| Discovering structural anomalies in graph-based data W Eberle, L Holder Seventh IEEE international conference on data mining workshops (ICDMW 2007 …, 2007 | 187 | 2007 |
| Anomaly detection in data represented as graphs W Eberle, L Holder Intelligent Data Analysis 11 (6), 663-689, 2007 | 175 | 2007 |
| Smart grid energy fraud detection using artificial neural networks V Ford, A Siraj, W Eberle 2014 IEEE symposium on computational intelligence applications in smart grid …, 2014 | 150 | 2014 |
| Explainable artificial intelligence approaches: A survey SR Islam, W Eberle, SK Ghafoor, M Ahmed arXiv preprint arXiv:2101.09429, 2021 | 90 | 2021 |
| Towards quantification of explainability in explainable artificial intelligence methods SR Islam, W Eberle, SK Ghafoor The thirty-third international flairs conference, 2020 | 56 | 2020 |
| A novel classifier ensemble approach for financial distress prediction D Liang, CF Tsai, AJ Dai, W Eberle Knowledge and Information Systems 54, 437-462, 2018 | 54 | 2018 |
| Mining for Structural Anomalies in Graph-based Data. W Eberle, LB Holder DMIN 7, 376-389, 2007 | 46 | 2007 |
| Domain knowledge aided explainable artificial intelligence for intrusion detection and response SR Islam, W Eberle, SK Ghafoor, A Siraj, M Rogers arXiv preprint arXiv:1911.09853, 2019 | 45 | 2019 |
| Graph-based approaches to insider threat detection W Eberle, L Holder Proceedings of the 5th annual workshop on cyber security and information …, 2009 | 43 | 2009 |
| Detecting dos attack in smart home iot devices using a graph-based approach R Paudel, T Muncy, W Eberle 2019 IEEE international conference on big data (big data), 5249-5258, 2019 | 38 | 2019 |
| Applying graph-based anomaly detection approaches to the discovery of insider threats W Eberle, L Holder 2009 IEEE International Conference on Intelligence and Security Informatics …, 2009 | 38 | 2009 |
| Data preprocessing issues for incomplete medical datasets MW Huang, WC Lin, CW Chen, SW Ke, CF Tsai, W Eberle Expert Systems 33 (5), 432-438, 2016 | 35 | 2016 |
| Learning to detect representative data for large scale instance selection WC Lin, CF Tsai, SW Ke, CW Hung, W Eberle Journal of Systems and Software 106, 1-8, 2015 | 33 | 2015 |
| Infusing domain knowledge in ai-based" black box" models for better explainability with application in bankruptcy prediction SR Islam, W Eberle, S Bundy, SK Ghafoor arXiv preprint arXiv:1905.11474, 2019 | 29 | 2019 |
| Mining illegal insider trading of stocks: A proactive approach SR Islam, SK Ghafoor, W Eberle 2018 IEEE international conference on big data (Big Data), 1397-1406, 2018 | 24 | 2018 |
| Anomaly detection of elderly patient activities in smart homes using a graph-based approach R Paudel, W Eberle, LB Holder Proceedings of the 2018 International Conference on Data Science, 163-169, 2018 | 24 | 2018 |
| Novel graph based anomaly detection using background knowledge S Velampalli, W Eberle The thirtieth international flairs conference, 2017 | 22 | 2017 |
| Identifying threats using graph-based anomaly detection W Eberle, L Holder, D Cook Machine Learning in Cyber Trust: Security, Privacy, and Reliability, 73-108, 2009 | 22 | 2009 |
