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All Outputs (124)

Discovering sequential patterns in a UK general practice database (2012)
Presentation / Conference Contribution
Reps, J., Garibaldi, J. M., Aickelin, U., Soria, D., E. Gibson, J. E., & Hubbard, R. B. Discovering sequential patterns in a UK general practice database. Presented at 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics

The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses. These patterns may be able to predict illnesses that a patient is likely to develop, allowin... Read More about Discovering sequential patterns in a UK general practice database.

An investigation into the relationship between type-2 FOU size and environmental uncertainty in robotic control (2012)
Presentation / Conference Contribution
Benatar, N., Aickelin, U., & Garibaldi, J. M. An investigation into the relationship between type-2 FOU size and environmental uncertainty in robotic control. Presented at 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

It has often been suggested that when faced with
large amount of uncertainty in situations of automated control type-2 fuzzy logic based controllers will out perform the simpler type-1 varieties due to the latter lacking a mechanism to model this un... Read More about An investigation into the relationship between type-2 FOU size and environmental uncertainty in robotic control.

MysiRNA-designer: a workflow for efficient siRNA design (2011)
Journal Article
Mysara, M., Garibaldi, J. M., & ElHefnawi, M. (2011). MysiRNA-designer: a workflow for efficient siRNA design. PLoS ONE, 6(10), Article e25642. https://doi.org/10.1371/journal.pone.0025642

The design of small interfering RNA (siRNA) is a multi factorial problem that has gained the attention of many researchers in the area of therapeutic and functional genomics. MysiRNA score was previously introduced that improves the correlation of si... Read More about MysiRNA-designer: a workflow for efficient siRNA design.

A "non-parametric" version of the naive Bayes classifier (2011)
Journal Article
Soria, D., Garibaldi, J. M., Ambrogi, F., Biganzoli, E. M., & Ellis, I. O. (2011). A "non-parametric" version of the naive Bayes classifier. Knowledge-Based Systems, 24(6), https://doi.org/10.1016/j.knosys.2011.02.014

Many algorithms have been proposed for the machine learning task of classication. One of the simplest methods, the naive Bayes classifyer, has often been found to give good performance despite the fact that its underlying assumptions (of independence... Read More about A "non-parametric" version of the naive Bayes classifier.

Learning pathway-based decision rules to classify microarray cancer samples (2010)
Presentation / Conference Contribution
Glaab, E., Garibaldi, J. M., & Krasnogor, N. Learning pathway-based decision rules to classify microarray cancer samples. Presented at 25th German Conference on Bioinformatics 2010

Despite recent advances in DNA chip technology current microarray gene expression studies are still affected by high noise levels, small sample sizes and large numbers of uninformative genes. Combining microarray data with cellular pathway data by us... Read More about Learning pathway-based decision rules to classify microarray cancer samples.

A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients (2010)
Journal Article
Soria, D., Garibaldi, J. M., Ambrogi, F., Green, A. R., Powe, D., Rakha, E., Douglas Macmillan, R., Blamey, R. W., Ball, G., Lisboa, P. J., Etchells, T. A., Boracchi, P., Biganzoli, E. M., & Ellis, I. O. (2010). A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients. Computers in Biology and Medicine, 40(3), https://doi.org/10.1016/j.compbiomed.2010.01.003

Single clustering methods have often been used to elucidate clusters in high dimensional medical data, even though reliance on a single algorithm is known to be problematic. In this paper, we present a methodology to determine a set of ‘core classes’... Read More about A methodology to identify consensus classes from clustering algorithms applied to immunohistochemical data from breast cancer patients.

A novel framework to elucidate core classes in a dataset (2010)
Presentation / Conference Contribution
Soria, D., & Garibaldi, J. M. A novel framework to elucidate core classes in a dataset. Presented at IEEE Congress on Evolutionary Computation (CEC) 2010

In this paper we present an original framework to extract representative groups from a dataset, and we validate it
over a novel case study. The framework specifies the application of different clustering algorithms, then several statistical and visu... Read More about A novel framework to elucidate core classes in a dataset.

Real-world transfer of evolved artificial immune system behaviours between small and large scale robotic platforms (2010)
Journal Article
Whitbrook, A., Aickelin, U., & Garibaldi, J. (2010). Real-world transfer of evolved artificial immune system behaviours between small and large scale robotic platforms. Evolutionary Intelligence, 3(3-4), https://doi.org/10.1007/s12065-010-0039-7

In mobile robotics, a solid test for adaptation is
the ability of a control system to function not only in a
diverse number of physical environments, but also on a
number of different robotic platforms. This paper demonstrates that a set of behavi... Read More about Real-world transfer of evolved artificial immune system behaviours between small and large scale robotic platforms.

Mimicking the behaviour of idiotypic AIS robot controllers using probabilistic systems (2009)
Presentation / Conference Contribution
Whitbrook, A., Whitbrook, A. M., Aickelin, U., & Garibaldi, J. M. (2009, July). Mimicking the behaviour of idiotypic AIS robot controllers using probabilistic systems. Paper presented at 13th World Multi-Conference on Systemics, Cybernetics and Informatics: WMSCI 2009

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 be a... Read More about Mimicking the behaviour of idiotypic AIS robot controllers using probabilistic systems.

Cancer profiles by Affinity Propagation (2009)
Journal Article
Soria, D., Garibaldi, J. M., Ambrogi, F., Boracchi, P., Raimondi, E., & Biganzoli, E. M. (2009). Cancer profiles by Affinity Propagation. International Journal of Knowledge Engineering and Soft Data Paradigms, 1(3), https://doi.org/10.1504/IJKESDP.2009.028814

The Affinity Propagation algorithm is applied to various problems of breast and cutaneous tumours subtyping using traditional biologic markers. The algorithm provides a procedure to determine the number of profiles to be considered. Well know breast... Read More about Cancer profiles by Affinity Propagation.

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), https://doi.org/10.2139/ssrn.2830328

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)
Presentation / Conference Contribution
Soria, D., Garibaldi, J. M., Biganzoli, E. M., & Ellis, I. O. A comparison of three different methods for classification of breast cancer data. Presented at Machine Learning and Applications 2008 (ICMLA'08) Seventh International Conference on Seventh International Conference on Machine Learning and Applications

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
Presentation / Conference Contribution
Benatar, N., Aickelin, U., & Garibaldi, J. M. A comparison of non-stationary, type-2 and dual surface fuzzy control. Presented at 2011 IEEE International Conference on Fuzzy Systems

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
Presentation / Conference Contribution
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. Presented at International Conference on Advances in Medical, Signal and Information Processing (4th)

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
Presentation / Conference Contribution
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. Presented at IEEE International Conference on Fuzzy Systems 2013 (Fuzz-IEEE 2013)

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
Presentation / Conference Contribution
Ladas, A., Aickelin, U., Garibaldi, J. M., & Ferguson, E. Using clustering to extract personality information from socio economic data. Presented at 12th UK Workshop on Computational Intelligence (UKCI 2012)

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
Presentation / Conference Contribution
Reps, J., Garibaldi, J. M., Aickelin, U., Soria, D., Gibson, J. E., & Hubbard, R. B. Comparing data-mining algorithms developed for longitudinal observational databases. Presented at UKCI 2012, the 12th Annual Workshop on Computational Intelligence

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
Presentation / Conference Contribution
Helmi, H., Garibaldi, J. M., & Aickelin, U. Examining the classification accuracy of TSVMs with feature selection in comparison with the GLAD algorithm. Presented at UKCI 2011, 11th Annual Workshop on Computational Intelligence

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
Presentation / Conference Contribution
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. Presented at UKCI 2011, 11th Annual Workshop on Computational Intelligence

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.