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ORCIDFrederic T. Stahl
Person information
- affiliation: German Research Center for Artificial Intelligence, Oldenburg, Germany
- affiliation (former): University of Reading, School of Systems Engineering
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2020 – today
- 2024
[c45]Christoph Tholen, Lars Nolle, Jochen Wollschlaeger, Frederic T. Stahl:
Model Generalisation For Predicting The Amount Of Photosynthetically Available Radiation In The Water Column From Freefall Profiler Observations. ECMS 2024: 381-386- 2023
- [c44]Lars Nolle, Frederic T. Stahl, Tarek A. El-Mihoub:
On Explanations for Hybrid Artificial Intelligence. SGAI Conf. 2023: 3-15 - [c43]Daniel Lukats
, Frederic T. Stahl:
On Reproducible Implementations in Unsupervised Concept Drift Detection Algorithms Research. SGAI Conf. 2023: 204-209 - [e3]Max Bramer, Frederic T. Stahl:
Artificial Intelligence XL - 43rd SGAI International Conference on Artificial Intelligence, AI 2023, Cambridge, UK, December 12-14, 2023, Proceedings. Lecture Notes in Computer Science 14381, Springer 2023, ISBN 978-3-031-47993-9 [contents] - 2022
- [j22]Mobin M. Idrees, Frederic T. Stahl, Atta Badii:
Adaptive Learning With Extreme Verification Latency in Non-Stationary Environments. IEEE Access 10: 127345-127364 (2022) - [c42]Frederic T. Stahl, Lars Nolle, Ahlem Jemai, Oliver Zielinski:
A Model For Predicting The Amount Of Photosynthetically Available Radiation From BGC-ARGO Float Observations In The Water Column. ECMS 2022: 174-180 - [c41]Ekene Frank Ozioko, Julian M. Kunkel, Frederic T. Stahl:
Speed Harmonisation Strategy for Human-Driven and Autonomous Vehicles Co-existence. SAI (3) 2022: 34-66 - [c40]Ekene Frank Ozioko, Julian M. Kunkel, Frederic T. Stahl:
Road Intersection Coordination Scheme for Mixed Traffic (Human Driven and Driver-Less Vehicles): A Systematic Review. SAI (3) 2022: 67-94 - [c39]Martin M. Kumm, Lars Nolle, Frederic T. Stahl, Ahlem Jemai, Oliver Zielinski:
On an Artificial Neural Network Approach for Predicting Photosynthetically Active Radiation in the Water Column. SGAI Conf. 2022: 112-123 - [c38]Manal Almutairi, Frederic T. Stahl, Max Bramer:
CRC: Consolidated Rules Construction for Expressive Ensemble Classification. SGAI Conf. 2022: 138-152 - [c37]Tarek A. El-Mihoub, Lars Nolle, Frederic T. Stahl:
Explainable Boosting Machines for Network Intrusion Detection with Features Reduction. SGAI Conf. 2022: 280-294 - [c36]Ahmed Abadulla Ashlam, Atta Badii, Frederic T. Stahl:
Multi-Phase Algorithmic Framework to Prevent SQL Injection Attacks using Improved Machine learning and Deep learning to Enhance Database security in Real-time. SIN 2022: 1-4 - [e2]Max Bramer, Frederic T. Stahl:
Artificial Intelligence XXXIX - 42nd SGAI International Conference on Artificial Intelligence, AI 2022, Cambridge, UK, December 13-15, 2022, Proceedings. Lecture Notes in Computer Science 13652, Springer 2022, ISBN 978-3-031-21440-0 [contents] - 2021
- [j21]Timothée Dubuc, Frederic T. Stahl, Etienne B. Roesch:
Mapping the Big Data Landscape: Technologies, Platforms and Paradigms for Real-Time Analytics of Data Streams. IEEE Access 9: 15351-15374 (2021) - [j20]Manal Almutairi, Frederic T. Stahl, Max Bramer:
ReG-Rules: An Explainable Rule-Based Ensemble Learner for Classification. IEEE Access 9: 52015-52035 (2021) - [j19]Frederic Theodor Stahl:
Guest Editorial. Expert Syst. J. Knowl. Eng. 38(7) (2021) - [j18]Frederic T. Stahl, Thien Le, Atta Badii, Mohamed Medhat Gaber:
A Frequent Pattern Conjunction Heuristic for Rule Generation in Data Streams. Inf. 12(1): 24 (2021) - [c35]Saif Alzubi, Frederic T. Stahl, Mohamed Medhat Gaber:
Towards Intrusion Detection Of Previously Unknown Network Attacks. ECMS 2021: 35-41 - [c34]Frederic T. Stahl, Oliver Ferdinand, Lars Nolle, Alexandra Pehlken, Oliver Zielinski:
AI Enabled Bio Waste Contamination-Scanner. SGAI Conf. 2021: 357-363 - 2020
- [j17]Mobin M. Idrees, Leandro L. Minku, Frederic T. Stahl, Atta Badii:
A heterogeneous online learning ensemble for non-stationary environments. Knowl. Based Syst. 188 (2020)
2010 – 2019
- 2019
- [c33]Chris Wrench, Frederic T. Stahl, Giuseppe Di Fatta, Vidhyalakshmi Karthikeyan, Detlef D. Nauck:
A Rule Induction Approach to Forecasting Critical Alarms in a Telecommunication Network. ICDM Workshops 2019: 480-489 - 2018
- [j16]Mahmood Hammoodi, Frederic T. Stahl, Atta Badii:
Real-time feature selection technique with concept drift detection using adaptive micro-clusters for data stream mining. Knowl. Based Syst. 161: 205-239 (2018) - [c32]Frederic Theodor Stahl:
Building Adaptive Data Mining Models on Streaming Data in Real-Time, an Outlook on Challenges, Approaches and Ongoing Research. ECMS 2018: 8-9 - [c31]Manal Almutairi, Frederic T. Stahl, Max Bramer:
A Rule-Based Classifier with Accurate and Fast Rule Term Induction for Continuous Attributes. ICMLA 2018: 413-420 - 2017
- [j15]Mark Tennant, Frederic T. Stahl, Omer F. Rana, João Bártolo Gomes:
Scalable real-time classification of data streams with concept drift. Future Gener. Comput. Syst. 75: 187-199 (2017) - [j14]Thien Le, Frederic T. Stahl, Mohamed Medhat Gaber, João Bártolo Gomes, Giuseppe Di Fatta:
On expressiveness and uncertainty awareness in rule-based classification for data streams. Neurocomputing 265: 127-141 (2017) - [c30]Manal Almutairi, Frederic T. Stahl, Max Bramer:
Improving Modular Classification Rule Induction with G-Prism Using Dynamic Rule Term Boundaries. SGAI Conf. 2017: 115-128 - 2016
- [j13]Mariam Adedoyin-Olowe, Mohamed Medhat Gaber, Carlos J. Martín-Dancausa, Frederic T. Stahl, João Bártolo Gomes:
A rule dynamics approach to event detection in Twitter with its application to sports and politics. Expert Syst. Appl. 55: 351-360 (2016) - [c29]Mahmood Hammoodi, Frederic T. Stahl, Mark Tennant:
Towards Online Concept Drift Detection with Feature Selection for Data Stream Classification. ECAI 2016: 1549-1550 - [c28]Thien Le, Frederic T. Stahl, Chris Wrench, Mohamed Medhat Gaber:
A Statistical Learning Method to Fast Generalised Rule Induction Directly from Raw Measurements. ICMLA 2016: 935-938 - [c27]Manal Almutairi, Frederic T. Stahl, Mathew Jennings, Thien Le, Max Bramer:
Towards Expressive Modular Rule Induction for Numerical Attributes. SGAI Conf. 2016: 229-235 - [c26]Chris Wrench, Frederic T. Stahl, Thien Le, Giuseppe Di Fatta, Vidhyalakshmi Karthikeyan, Detlef D. Nauck:
A Method of Rule Induction for Predicting and Describing Future Alarms in a Telecommunication Network. SGAI Conf. 2016: 309-323 - 2015
- [j12]Indre Zliobaite, Marcin Budka, Frederic T. Stahl:
Towards cost-sensitive adaptation: When is it worth updating your predictive model? Neurocomputing 150: 240-249 (2015) - [j11]Frederic T. Stahl, David May, Hugo Mills, Max Bramer, Mohamed Medhat Gaber:
A Scalable Expressive Ensemble Learning Using Random Prism: A MapReduce Approach. Trans. Large Scale Data Knowl. Centered Syst. 20: 90-107 (2015) - [c25]Sami Al Ghamdi, Giuseppe Di Fatta, Frederic T. Stahl:
Optimisation Techniques for Parallel K-Means on MapReduce. IDCS 2015: 193-200 - [c24]Mark Tennant, Frederic T. Stahl, João Bártolo Gomes:
Fast Adaptive Real-Time Classification for Data Streams with Concept Drift. IDCS 2015: 265-272 - [c23]Chris Wrench, Frederic T. Stahl, Giuseppe Di Fatta, Vidhyalakshmi Karthikeyan, Detlef D. Nauck:
Towards Expressive Rule Induction on IP Network Event Streams. SGAI Conf. 2015: 191-196 - [e1]Giuseppe Di Fatta, Giancarlo Fortino, Wenfeng Li, Mukaddim Pathan, Frederic T. Stahl, Antonio Guerrieri:
Internet and Distributed Computing Systems - 8th International Conference, IDCS 2015, Windsor, UK, September 2-4, 2015. Proceedings. Lecture Notes in Computer Science 9258, Springer 2015, ISBN 978-3-319-23236-2 [contents] - 2014
- [j10]Frederic T. Stahl, Max Bramer:
Random Prism: a noise-tolerant alternative to Random Forests. Expert Syst. J. Knowl. Eng. 31(5): 411-420 (2014) - [j9]Mariam Adedoyin-Olowe, Mohamed Medhat Gaber, Frederic T. Stahl:
A Survey of Data Mining Techniques for Social Media Analysis. J. Data Min. Digit. Humanit. 2014 (2014) - [j8]Mohamed Medhat Gaber, João Gama, Shonali Krishnaswamy, João Bártolo Gomes, Frederic T. Stahl:
Data stream mining in ubiquitous environments: state-of-the-art and current directions. WIREs Data Mining Knowl. Discov. 4(2): 116-138 (2014) - [c22]Han Liu, Alexander E. Gegov, Frederic T. Stahl:
Categorization and Construction of Rule Based Systems. EANN 2014: 183-194 - [c21]Mariam Adedoyin-Olowe, Mohamed Medhat Gaber, Carlos J. Martín-Dancausa, Frederic T. Stahl:
Extraction of Unexpected Rules from Twitter Hashtags and its Application to Sport Events. ICMLA 2014: 207-212 - [c20]Thien Le, Frederic T. Stahl, João Bártolo Gomes, Mohamed Medhat Gaber, Giuseppe Di Fatta:
Computationally Efficient Rule-Based Classification for Continuous Streaming Data. SGAI Conf. 2014: 21-34 - [c19]Mark Tennant, Frederic T. Stahl, Giuseppe Di Fatta, João Bártolo Gomes:
Towards a Parallel Computationally Efficient Approach to Scaling Up Data Stream Classification. SGAI Conf. 2014: 51-65 - 2013
- [j7]Frederic T. Stahl, Max Bramer:
Scaling up classification rule induction through parallel processing. Knowl. Eng. Rev. 28(4): 451-478 (2013) - [j6]Frederic T. Stahl, Bogdan Gabrys, Mohamed Medhat Gaber, Monika Berendsen:
An overview of interactive visual data mining techniques for knowledge discovery. WIREs Data Mining Knowl. Discov. 3(4): 239-256 (2013) - [c18]Mariam Adedoyin-Olowe, Mohamed Medhat Gaber, Frederic T. Stahl:
TRCM: A Methodology for Temporal Analysis of Evolving Concepts in Twitter. ICAISC (2) 2013: 135-145 - [c17]João Bártolo Gomes, Mariam Adedoyin-Olowe, Mohamed Medhat Gaber, Frederic T. Stahl:
Rule Type Identification Using TRCM for Trend Analysis in Twitter. SGAI Conf. 2013: 273-278 - [c16]Peter Rausch, Frederic T. Stahl, Michael Stumpf:
Efficient Interactive Budget Planning and Adjusting Under Financial Stress. SGAI Conf. 2013: 375-388 - [i1]Mariam Adedoyin-Olowe, Mohamed Medhat Gaber, Frederic T. Stahl:
A Survey of Data Mining Techniques for Social Media Analysis. CoRR abs/1312.4617 (2013) - 2012
- [j5]Frederic T. Stahl, Max Bramer:
Jmax-pruning: A facility for the information theoretic pruning of modular classification rules. Knowl. Based Syst. 29: 12-19 (2012) - [j4]Frederic T. Stahl, Max Bramer:
Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks. Knowl. Based Syst. 35: 49-63 (2012) - [j3]Frederic T. Stahl, Mohamed Medhat Gaber, Paul Aldridge, David May, Han Liu, Max Bramer, Philip S. Yu:
Homogeneous and Heterogeneous Distributed Classification for Pocket Data Mining. Trans. Large Scale Data Knowl. Centered Syst. 5: 183-205 (2012) - [j2]Frederic T. Stahl, Ivan Jordanov:
An overview of the use of neural networks for data mining tasks. WIREs Data Mining Knowl. Discov. 2(3): 193-208 (2012) - [c15]Frederic T. Stahl, David May, Max Bramer:
Parallel Random Prism: A Computationally Efficient Ensemble Learner for Classification. SGAI Conf. 2012: 21-34 - [c14]Frederic T. Stahl, Mohamed Medhat Gaber, Manuel Martin Salvador:
eRules: A Modular Adaptive Classification Rule Learning Algorithm for Data Streams. SGAI Conf. 2012: 65-78 - 2011
- [c13]Frederic T. Stahl, Mohamed Medhat Gaber, Max Bramer, Philip S. Yu:
Distributed hoeffding trees for pocket data mining. HPCS 2011: 686-692 - [c12]Frederic T. Stahl, Mohamed Medhat Gaber, Han Liu, Max Bramer, Philip S. Yu:
Distributed Classification for Pocket Data Mining. ISMIS 2011: 336-345 - [c11]Frederic T. Stahl, Max Bramer:
Random Prism: An Alternative to Random Forests. SGAI Conf. 2011: 5-18 - 2010
- [j1]Martin T. Swain, Cândida G. Silva, Nuno Loureiro-Ferreira, Vitaliy Ostropytskyy, João Brito, Olivier Riche, Frederic T. Stahl, Werner Dubitzky, Rui M. M. Brito:
P-found: Grid-enabling distributed repositories of protein folding and unfolding simulations for data mining. Future Gener. Comput. Syst. 26(3): 424-433 (2010) - [c10]Frederic T. Stahl, Mohamed Medhat Gaber, Max Bramer, Philip S. Yu:
Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments. ICTAI (2) 2010: 323-330 - [c9]Frederic T. Stahl, Max Bramer, Mo Adda:
J-PMCRI: A Methodology for Inducing Pre-pruned Modular Classification Rules. IFIP AI 2010: 47-56 - [c8]Frederic T. Stahl, Max Bramer:
Induction of Modular Classification Rules: Using Jmax-pruning. SGAI Conf. 2010: 79-92
2000 – 2009
- 2009
- [b1]Frederic Theodor Stahl:
Parallel rule induction. University of Portsmouth, UK, 2009 - [c7]Frederic T. Stahl, Max A. Bramer, Mo Adda:
PMCRI: A Parallel Modular Classification Rule Induction Framework. MLDM 2009: 148-162 - [c6]Frederic T. Stahl, Max Bramer, Mo Adda:
Parallel Rule Induction with Information Theoretic Pre-Pruning. SGAI Conf. 2009: 151-164 - 2008
- [c5]Martin T. Swain, Vitaliy Ostropytskyy, Cândida G. Silva, Frederic T. Stahl, Olivier Riche, Rui M. M. Brito, Werner Dubitzky:
Grid Computing Solutions for Distributed Repositories of Protein Folding and Unfolding Simulations. ICCS (3) 2008: 70-79 - [c4]Frederic T. Stahl, Max A. Bramer, Mo Adda:
P-Prism: A Computationally Efficient Approach to Scaling up Classification Rule Induction. IFIP AI 2008: 77-86 - [c3]Frederic T. Stahl, Max Bramer, Mo Adda:
Parallel Induction of Modular Classification Rules. SGAI Conf. 2008: 343-348 - 2007
- [c2]Frederic T. Stahl, Max Bramer:
Towards a Computationally Efficient Approach to Modular Classification Rule Induction. SGAI Conf. 2007: 357-362 - 2005
- [c1]Frederic T. Stahl, Daniel P. Berrar, Cândida G. Silva, R. J. Rodrigues, Rui M. M. Brito, Werner Dubitzky:
Grid warehousing of molecular dynamics protein unfolding data. CCGRID 2005: 496-503
Coauthor Index
aka: Max A. Bramer
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