School of Computing

Profile

Profile:

Research Interest: Machine Learning, Distributed and Parallel Data Mining and Mining of Data Streams

Research Group: artificial intelligence and intelligent systems.

Publications:

Stahl, F., Bramer, M. (in press). Scaling Up Classification Rule Induction Through Parallel Processing. Knowledge Engineering Review.

Stahl, F., Bramer, M., Adda, M. (2011). Induction of Modular Classification Rules: Using Jmax-pruning. In Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge: Springer.

Stahl, F., Gaber, M. M., Bramer, M., Yu, P. S., Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments. Proceedings of the IEEE Twenty-second International Conference on Tools with Artificial Intelligence (ICTAI 2010), Arras, France, 27-29 October, 2010.

Stahl, F., Bramer, M., Adda, M. (2010). J-PMCRI: A Methodology for Inducing Pre-pruned Modular Classification Rules. In Twenty-first IFIP World Computer Congress, pp. 47-56, Brisbane: Springer.

Stahl, F., Bramer, M., Adda, M. (2010). Parallel Rule Induction with Information Theoretic Pre-Pruning. In Twenty-ninth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge: Springer.

Stahl, F. (2009). Parallel Rule Induction. Doctoral dissertation, Portsmouth University.

Swain, M., Goncalves, C.S., Loureiro-Ferreira, N., Ostropytskyy, V., Brito, J., Riche, O., Stahl, F., Dubitzky., W., Brito, R.M.M. (2009). P-found: Grid-enabling distributed repositories of protein folding and unfolding simulations for data mining. Future Generation Computer Systems.

Stahl, F., Bramer, M., Adda, M. (2009). PMCRI: A Parallel Modular Classification Rule Induction Framework, InSixth International Conference on Machine Learning and Data Mining. Springer, pp. 148-162, Leipzig: Springer.

Swain, M., Ostropytskyy, V., Goncalves, C.S., Stahl, F., Riche, O., Brito, R.M.M., Dubitzky., W. (2008). Grid Computing Solutions for Distributed Repositories of Protein Folding and Unfolding Simulations. In the 2008 International Conference on Computational Science, Part III, pp. 70-79, Kraków: Springer.

Stahl, F., Bramer, M., Adda, M. (2008). Parallel Induction of Modular Classification Rules. In Twenty-eighth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge: Springer.

Stahl, F., Bramer, M., Adda, M. (2008). P-Prism: A Computationally Efficient Approach to Scaling up Classification Rule Induction. In Twentieth IFIP World Computer Congress, pp. 77-86, Milan: Springer.

Stahl. F., Bramer, M. (2007). Towards a Computationally Efficient Approach to Modular Classification Rule Induction. In Twenty-seventh SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 357-362, Cambridge: Springer.

Berrar, D., Stahl, F., Goncalves, C.S., Rodrigues, J.R ., Brito, R.M.M., Dubitzky W. (2005). Towards Data Warehousing and Mining of Protein Unfolding Simulation Data. Journal of Clinical Monitoring and Computing, 19 (4-5), 307-317.

Stahl, F., Berrar, D., Goncalves, C.S., Rodrigues, J.R., Brito, R.M.M., Dubitzky, W. (2005). Grid Warehousing of Molecular Dynamics Protein Unfolding Data. In Proc. of the Fifth IEEE/ACM Int'l Symposium on Cluster Computing and the Grid, pp. 496-503,Cardiff.