All Outputs (113)

The use of probabilistic systems to mimic the behaviour of idiotypic AIS robot controllers (2009)
Journal Article
Whitbrook, A., Aickelin, U., & Garibaldi, J. M. (2009). The use of probabilistic systems to mimic the behaviour of idiotypic AIS robot controllers. Journal of Systemics, Cybernetics and Informatics, 7(6),

Previous work has shown that robot navigation systems that employ an architecture based upon the idiotypic network theory of the immune system have an advantage over control techniques that rely on reinforcement learning only. This is thought to b... Read More about The use of probabilistic systems to mimic the behaviour of idiotypic AIS robot controllers.

A comparison of three different methods for classification of breast cancer data (2008)
Conference Proceeding
Soria, D., Garibaldi, J. M., Biganzoli, E. M., & Ellis, I. O. (2008). A comparison of three different methods for classification of breast cancer data.

The classification of breast cancer patients is of great importance in cancer diagnosis. During the last few years, many algorithms have been proposed for this task. In this paper, we review different supervised machine learning techniques for classi... Read More about A comparison of three different methods for classification of breast cancer data.

A comparison of non-stationary, type-2 and dual surface fuzzy control
Conference Proceeding
Benatar, N., Aickelin, U., & Garibaldi, J. M. (in press). A comparison of non-stationary, type-2 and dual surface fuzzy control.

Type-1 fuzzy logic has frequently been used in control systems. However this method is sometimes shown to be too restrictive and unable to adapt in the presence of uncertainty. In this paper we compare type-1 fuzzy control with several other fuzzy ap... Read More about A comparison of non-stationary, type-2 and dual surface fuzzy control.

Clustering breast cancer data by consensus of different validity indices
Conference Proceeding
Soria, D., Garibaldi, J. M., Ambrogi, F., Lisboa, P. J., Boracchi, P., & Biganzoli, E. M. Clustering breast cancer data by consensus of different validity indices.

Clustering algorithms will, in general, either partition a given data set into a pre-specified number of clusters or will produce a hierarchy of clusters. In this paper we analyse several different clustering techniques and apply them to a particular... Read More about Clustering breast cancer data by consensus of different validity indices.

Performance measurement under increasing environmental uncertainty in the context of interval type-2 fuzzy logic based robotic sailing
Conference Proceeding
Benatar, N., Aickelin, U., & Garibaldi, J. M. Performance measurement under increasing environmental uncertainty in the context of interval type-2 fuzzy logic based robotic sailing.

Performance measurement of robotic controllers based on fuzzy logic, operating under uncertainty, is a subject area which has been somewhat ignored in the current literature. In this paper standard measures such as RMSE are shown to be inappropriate... Read More about Performance measurement under increasing environmental uncertainty in the context of interval type-2 fuzzy logic based robotic sailing.

Using clustering to extract personality information from socio economic data
Conference Proceeding
Ladas, A., Aickelin, U., Garibaldi, J. M., & Ferguson, E. Using clustering to extract personality information from socio economic data.

It has become apparent that models that have been applied widely in economics, including Machine Learning techniques and Data Mining methods, should take into consideration principles that derive from the theories of Personality Psychology in order t... Read More about Using clustering to extract personality information from socio economic data.

Comparing data-mining algorithms developed for longitudinal observational databases
Conference Proceeding
Reps, J., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. Comparing data-mining algorithms developed for longitudinal observational databases.

Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detecting negative side effects. Algorithms mining longitudinal observation... Read More about Comparing data-mining algorithms developed for longitudinal observational databases.

Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm
Conference Proceeding
Helmi, H., Garibaldi, J. M., & Aickelin, U. Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm.

Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function pr... Read More about Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm.

Investigating the detection of adverse drug events in a UK general practice electronic health-care database
Conference Proceeding
Reps, J., Feyereisl, J., Garibaldi, J. M., Aickelin, U., Gibson, J. E., & Hubbard, R. B. Investigating the detection of adverse drug events in a UK general practice electronic health-care database.

Data-mining techniques have frequently been developed for Spontaneous reporting databases. These techniques aim to find adverse drug events accurately and efficiently. Spontaneous reporting databases are prone to missing information,under reporting... Read More about Investigating the detection of adverse drug events in a UK general practice electronic health-care database.

The transfer of evolved artificial immune system behaviours between small and large scale robotic platforms
Book Chapter
Whitbrook, A., Aickelin, U., & Garibaldi, J. M. The transfer of evolved artificial immune system behaviours between small and large scale robotic platforms. In P. Collet, N. Monmarché, P. Legrand, M. Schoenauer, & E. Lutton (Eds.), Artificial evolution: 9th International Conference = Evolution Artificielle, EA 2009: Strasbourg, France, October 26-28, 2009: revised selected papers. Springer

This paper demonstrates that a set of behaviours evolved in simulation on a miniature robot (epuck) can be transferred to a much larger scale platform (a virtual Pioneer P3-DX) that also differs in shape, sensor type, sensor configuration and progra... Read More about The transfer of evolved artificial immune system behaviours between small and large scale robotic platforms.

Two-timescale learning using idiotypic behaviour mediation for a navigating mobile robot
Journal Article
Whitbrook, A., Aickelin, U., & Garibaldi, J. M. Two-timescale learning using idiotypic behaviour mediation for a navigating mobile robot. Applied Soft Computing, 10(3), https://doi.org/10.1016/j.asoc.2009.10.005

A combined short-term learning (STL) and long-term learning (LTL) approach to solving mobile-robot navigation problems is presented and tested in both the real and virtual domains. The LTL phase consists of rapid simulations that use a genetic algo... Read More about Two-timescale learning using idiotypic behaviour mediation for a navigating mobile robot.

An idiotypic immune network as a short-term learning architecture for mobile robots
Book Chapter
Whitbrook, A., Aickelin, U., & Garibaldi, J. M. An idiotypic immune network as a short-term learning architecture for mobile robots. In P. Bentley, D. Lee, & S. Jung (Eds.), Artificial immune systems: 7th international conference, ICARIS 2008, Phuket, Thailand, August 10-13, 2008: proceedings. Springer

A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations that use a Genetic Algorith... Read More about An idiotypic immune network as a short-term learning architecture for mobile robots.

Genetic algorithm seeding of idiotypic networks for mobile-robot navigation
Conference Proceeding
Whitbrook, A., Aickelin, U., & Garibaldi, J. M. Genetic algorithm seeding of idiotypic networks for mobile-robot navigation.

Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial... Read More about Genetic algorithm seeding of idiotypic networks for mobile-robot navigation.