Parallel data mining-case study
(2010)
Report
Tekiner, F., Pettipher, M., Bull, L., Studley, M., Whittley, I., & Bagnall, T. (2010). Parallel data mining-case study
Outputs (6)
Learning classifier system ensembles with rule-sharing (2007)
Journal Article
Bull, L., Studley, M., Bagnall, A., & Whittley, I. (2007). Learning classifier system ensembles with rule-sharing. IEEE Transactions on Evolutionary Computation, 11(4), 496-502. https://doi.org/10.1109/TEVC.2006.885163This paper presents an investigation into exploiting the population-based nature of learning classifier systems (LCSs) for their use within highly parallel systems. In particular, the use of simple payoff and accuracy-based LCSs within the ensemble m... Read More about Learning classifier system ensembles with rule-sharing.
A comparison of DWT/PAA and DFT for time series classification (2006)
Presentation / Conference
Bagnall, A. J., Whittley, I. M., Janacek, G. J., Kemsley, K., Studley, M., & Bull, L. (2006, June). A comparison of DWT/PAA and DFT for time series classification. Paper presented at International conference on Data Mining (DMIN '06), Las Vegas, USDiscrete Fourier transforms (DFT) and Haar two, PAA is identical to a Haar Wavelet transformation [61. wavelets (DWT) were proposed for the use in time series data mining over five years ago and have since proved to be popular algorithms for the tran... Read More about A comparison of DWT/PAA and DFT for time series classification.
On the use of rule-sharing in learning classifier system ensembles (2005)
Presentation / Conference
Bull, L., Studley, M., Bagnall, T., & Whittley, I. (2005, June). On the use of rule-sharing in learning classifier system ensembles. Paper presented at The 2005 IEEE Congress on Evolutionary Computation
Attribute selection methods for filtered attribute subspace based bagging with injected randomness (FASBIR) (2005)
Presentation / Conference
Whittley, I., Bagnall, A., Bull, L., Pettipher, M., Studley, M., & Tekiner, F. (2005, April). Attribute selection methods for filtered attribute subspace based bagging with injected randomness (FASBIR). Paper presented at 2005 SIAM International Conference on Data Mining, Newport Beach, California
Consideration of multiple objectives in neural learning classifier systems (2002)
Conference Proceeding
Bull, L., & Studley, M. (2002). Consideration of multiple objectives in neural learning classifier systems. In J. J. Merelo, P. Adamidis, & H. Beyer (Eds.), Parallel Problem Solving from Nature—PPSN VII. , (549-557). https://doi.org/10.1007/3-540-45712-7_53© Springer-Verlag Berlin Heidelberg 2002. For effective use in a number of problem domains Learning Classifier Systems must be able to manage multiple objectives. This paper explicitly considers the case of developing the controller for a simulated m... Read More about Consideration of multiple objectives in neural learning classifier systems.