Frederic T. Stahl

发表

Distributed hoeffding trees for pocket data mining

Philip S. Yu, Mohamed Medhat Gaber, Max Bramer, 2011, 2011 International Conference on High Performance Computing & Simulation.

Random Prism: a noise-tolerant alternative to Random Forests

Max Bramer, Frederic T. Stahl, 2014, Expert Syst. J. Knowl. Eng..

Random Prism: An Alternative to Random Forests

Max Bramer, Frederic T. Stahl, M. Bramer, 2011, SGAI Conf..

A Statistical Learning Method to Fast Generalised Rule Induction Directly from Raw Measurements

Mohamed Medhat Gaber, Frederic T. Stahl, Thien Le, 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).

Parallel Rule Induction with Information Theoretic Pre-Pruning

Mo Adda, Max Bramer, Frederic T. Stahl, 2009, SGAI Conf..

Scalable real-time classification of data streams with concept drift

João Bártolo Gomes, Omer F. Rana, Frederic T. Stahl, 2017, Future Gener. Comput. Syst..

Extraction of Unexpected Rules from Twitter Hashtags and its Application to Sport Events

Mohamed Medhat Gaber, Mariam Adedoyin-Olowe, Carlos J. Martín-Dancausa, 2014, 2014 13th International Conference on Machine Learning and Applications.

An overview of the use of neural networks for data mining tasks

Ivan Jordanov, Frederic T. Stahl, I. Jordanov, 2012, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

Parallel Induction of Modular Classification Rules

Mo Adda, Max Bramer, Frederic T. Stahl, 2008, SGAI Conf..

A rule dynamics approach to event detection in Twitter with its application to sports and politics

Mohamed Medhat Gaber, João Bártolo Gomes, Mariam Adedoyin-Olowe, 2016, Expert Syst. Appl..

Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments

Philip S. Yu, Mohamed Medhat Gaber, Max Bramer, 2010, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence.

Towards a Parallel Computationally Efficient Approach to Scaling Up Data Stream Classification

Giuseppe Di Fatta, João Bártolo Gomes, Frederic T. Stahl, 2014, SGAI Conf..

eRules: A Modular Adaptive Classification Rule Learning Algorithm for Data Streams

Mohamed Medhat Gaber, Frederic T. Stahl, Manuel Martin Salvador, 2012, SGAI Conf..

Grid warehousing of molecular dynamics protein unfolding data

Werner Dubitzky, Daniel P. Berrar, Frederic T. Stahl, 2005, CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005..

Real-time feature selection technique with concept drift detection using adaptive micro-clusters for data stream mining

Atta Badii, Frederic T. Stahl, Mahmood Hammoodi, 2018, Knowl. Based Syst..

Towards Expressive Rule Induction on IP Network Event Streams

Giuseppe Di Fatta, Detlef D. Nauck, Frederic T. Stahl, 2015, SGAI Conf..

Parallel Random Prism: A Computationally Efficient Ensemble Learner for Classification

Max Bramer, Frederic T. Stahl, David May, 2012, SGAI Conf..

A Method of Rule Induction for Predicting and Describing Future Alarms in a Telecommunication Network

Giuseppe Di Fatta, Detlef D. Nauck, Frederic T. Stahl, 2016, SGAI Conf..

Computationally efficient induction of classification rules with the PMCRI and J-PMCRI frameworks

Max Bramer, Frederic T. Stahl, M. Bramer, 2012, Knowl. Based Syst..

Scaling up classification rule induction through parallel processing

Max Bramer, Frederic T. Stahl, M. Bramer, 2012, The Knowledge Engineering Review.

Homogeneous and Heterogeneous Distributed Classification for Pocket Data Mining

Philip S. Yu, Mohamed Medhat Gaber, Han Liu, 2012, Trans. Large Scale Data Knowl. Centered Syst..

J-PMCRI: A Methodology for Inducing Pre-pruned Modular Classification Rules

Mo Adda, Max Bramer, Frederic T. Stahl, 2010, IFIP AI.

TRCM: A Methodology for Temporal Analysis of Evolving Concepts in Twitter

Mohamed Medhat Gaber, Mariam Adedoyin-Olowe, Frederic T. Stahl, 2013, ICAISC.

Distributed Classification for Pocket Data Mining

Philip S. Yu, Mohamed Medhat Gaber, Han Liu, 2011, ISMIS.

Towards Online Concept Drift Detection with Feature Selection for Data Stream Classification

Frederic T. Stahl, Mark Tennant, Mahmood Hammoodi, 2016, ECAI.

Categorization and Construction of Rule Based Systems

Alexander E. Gegov, Han Liu, Frederic T. Stahl, 2014, EANN.

A Scalable Expressive Ensemble Learning Using Random Prism: A MapReduce Approach

Mohamed Medhat Gaber, Max Bramer, Hugo Mills, 2015, Trans. Large Scale Data Knowl. Centered Syst..

An overview of interactive visual data mining techniques for knowledge discovery

Mohamed Medhat Gaber, Bogdan Gabrys, Frederic T. Stahl, 2013, WIREs Data Mining Knowl. Discov..

Towards Expressive Modular Rule Induction for Numerical Attributes

Max Bramer, Frederic T. Stahl, Thien Le, 2016, SGAI Conf..

Fast Adaptive Real-Time Classification for Data Streams with Concept Drift

João Bártolo Gomes, Frederic T. Stahl, Mark Tennant, 2015, IDCS.

Rule Type Identification Using TRCM for Trend Analysis in Twitter

Mohamed Medhat Gaber, João Bártolo Gomes, Mariam Adedoyin-Olowe, 2013, SGAI Conf..

Efficient Interactive Budget Planning and Adjusting Under Financial Stress

Frederic T. Stahl, Peter Rausch, Michael Stumpf, 2013, SGAI Conf..

PMCRI: A Parallel Modular Classification Rule Induction Framework

Mo Adda, Max Bramer, Frederic T. Stahl, 2009, MLDM.

On expressiveness and uncertainty awareness in rule-based classification for data streams

Giuseppe Di Fatta, Mohamed Medhat Gaber, João Bártolo Gomes, 2017, Neurocomputing.

P-Prism: A Computationally Efficient Approach to Scaling up Classification Rule Induction

Mo Adda, Max Bramer, Frederic T. Stahl, 2008, IFIP AI.

Data stream mining in ubiquitous environments: state‐of‐the‐art and current directions

Mohamed Medhat Gaber, João Bártolo Gomes, Shonali Krishnaswamy, 2014, WIREs Data Mining Knowl. Discov..

P-found: Grid-enabling distributed repositories of protein folding and unfolding simulations for data mining

Werner Dubitzky, Martin T. Swain, Frederic T. Stahl, 2010, Future Gener. Comput. Syst..

Induction of Modular Classification Rules: Using Jmax-pruning

Max Bramer, Frederic T. Stahl, 2010, SGAI Conf..

Jmax-pruning: A facility for the information theoretic pruning of modular classification rules

Max Bramer, Frederic T. Stahl, M. Bramer, 2012, Knowl. Based Syst..

Towards a Computationally Efficient Approach to Modular Classification Rule Induction

Max Bramer, Frederic T. Stahl, 2007, SGAI Conf..

Improving Modular Classification Rule Induction with G-Prism Using Dynamic Rule Term Boundaries

Max Bramer, Frederic T. Stahl, Manal Almutairi, 2017, SGAI Conf..

Optimisation Techniques for Parallel K-Means on MapReduce

Giuseppe Di Fatta, Frederic T. Stahl, Sami Al Ghamdi, 2015, IDCS.

Grid Computing Solutions for Distributed Repositories of Protein Folding and Unfolding Simulations

Werner Dubitzky, Martin T. Swain, Frederic T. Stahl, 2008, ICCS.

Computationally Efficient Rule-Based Classification for Continuous Streaming Data

Giuseppe Di Fatta, Mohamed Medhat Gaber, João Bártolo Gomes, 2014, SGAI Conf..

A Survey of Data Mining Techniques for Social Media Analysis pdf

Mohamed Medhat Gaber, Mariam Adedoyin-Olowe, Frederic T. Stahl, 2013, J. Data Min. Digit. Humanit..

Towards cost-sensitive adaptation: When is it worth updating your predictive model?

Marcin Budka, Indre Zliobaite, Frederic T. Stahl, 2015, Neurocomputing.

A Rule-Based Classifier with Accurate and Fast Rule Term Induction for Continuous Attributes

Max Bramer, Frederic T. Stahl, Manal Almutairi, 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

A Frequent Pattern Conjunction Heuristic for Rule Generation in Data Streams

Mohamed Medhat Gaber, Atta Badii, Frederic T. Stahl, 2021, Inf..

Towards Intrusion Detection Of Previously Unknown Network Attacks

Mohamed Medhat Gaber, Frederic T. Stahl, Saif Alzubi, 2021, ECMS.

A Survey of Data Mining Techniques for Social Media Analysis

Frederic T. Stahl, M. Gaber, F. Stahl, 2013, J. Data Min. Digit. Humanit..

Machine learning for aquatic plastic litter detection, classification and quantification (APLASTIC-Q)

Frederic T. Stahl, F. Stahl, O. Zielinski, 2020, Environmental Research Letters.

Unified framework for construction of rule based classification systems

Han Liu, Frederic Stahl, Frederic T. Stahl, 2015 .

J-measure based hybrid pruning for complexity reduction in classification rules

Han Liu, Frederic Stahl, Frederic T. Stahl, 2013 .

A heterogeneous online learning ensemble for non-stationary environments

Leandro L. Minku, Frederic Stahl, Atta Badii, 2020, Knowl. Based Syst..

University of Birmingham A heterogeneous online learning ensemble for non- stationary environments

Leandro L. Minku, Frederic T. Stahl, Mobin M. Idrees, 2019 .

ReG-Rules: An Explainable Rule-Based Ensemble Learner for Classification

Frederic Stahl, Max Bramer, Frederic T. Stahl, 2021, IEEE Access.

Towards real-time feature tracking technique using adaptive micro-clusters

Atta Badii, Frederic T. Stahl, Mark Tennant, 2017 .

Pocket Data Mining

Mohamed Medhat Gaber, Frederic Stahl, Frederic T. Stahl, 2014 .

A text mining framework for Big Data

Frederic Stahl, Frederic T. Stahl, Niki Pavlopoulou, 2017 .

Mining Framework for Big Data

Frederic T. Stahl, F. Stahl, Niki Pavlopoulou, 2017 .

scalable expressive ensemble learning using Random Prism : a MapReduce approach . Transactions on Large Scale Data

Frederic T. Stahl, M. Bramer, F. Stahl, 2018 .

Autonomic Discovery of News Evolvement in Twitter

Mohamed Medhat Gaber, João Bártolo Gomes, Mariam Adedoyin-Olowe, 2015 .

Mapping the Big Data Landscape: Technologies, Platforms and Paradigms for Real-Time Analytics of Data Streams

Etienne B. Roesch, Frederic Stahl, Frederic T. Stahl, 2021, IEEE Access.

Towards Data Warehousing and Mining of Protein Unfolding Simulation Data

Werner Dubitzky, Daniel Berrar, Frederic T. Stahl, 2005, Journal of Clinical Monitoring and Computing.