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All Outputs (14)

Analysing fuzzy sets through combining measures of similarity and distance (2014)
Conference Proceeding
McCulloch, J., Wagner, C., & Aickelin, U. (2014). Analysing fuzzy sets through combining measures of similarity and distance.

Reasoning with fuzzy sets can be achieved through measures such as similarity and distance. However, these measures can often give misleading results when considered independently, for example giving the same value for two different pairs of fuzzy se... Read More about Analysing fuzzy sets through combining measures of similarity and distance.

Comparison of distance metrics for hierarchical data in medical databases (2014)
Conference Proceeding
Hassan, D., Aickelin, U., & Wagner, C. (2014). Comparison of distance metrics for hierarchical data in medical databases.

Distance metrics are broadly used in different research areas and applications, such as bio-informatics, data mining and many other fields. However, there are some metrics, like pg-gram and Edit Distance used specifically for data with a hierarchical... Read More about Comparison of distance metrics for hierarchical data in medical databases.

Augmented Neural Networks for modelling consumer indebtness (2014)
Journal Article
Ladas, A., M. Garibaldi, J., Scarpel, R., & Aickelin, U. (2014). Augmented Neural Networks for modelling consumer indebtness. Proceedings of International Joint Conference on Neural Networks, 3086-3093. https://doi.org/10.1109/IJCNN.2014.6889760

Consumer Debt has risen to be an important problem of modern societies, generating a lot of research in order to understand the nature of consumer indebtness, which so far its modelling has been carried out by statistical models. In this work we show... Read More about Augmented Neural Networks for modelling consumer indebtness.

Towards the development of a simulator for investigating the impact of people management practices on retail performance (2014)
Book Chapter
Siebers, P., Aickelin, U., Celia, H., & Clegg, C. (2014). Towards the development of a simulator for investigating the impact of people management practices on retail performance. In S. J. Taylor (Ed.), Agent-based modeling and simulation (97-132). Palgrave Macmillan. https://doi.org/10.1057/9781137453648_7

Often models for understanding the impact of management practices on retail performance are developed under the assumption of stability, equilibrium and linearity, whereas retail operations are considered in reality to be dynamic, non-linear and comp... Read More about Towards the development of a simulator for investigating the impact of people management practices on retail performance.

A fuzzy directional distance measure (2014)
Conference Proceeding
McCulloch, J., Hinde, C., Wagner, C., & Aickelin, U. (2014). A fuzzy directional distance measure.

The measure of distance between two fuzzy sets is a fundamental tool within fuzzy set theory, however, distance measures currently within the literature use a crisp value to represent the distance between fuzzy sets. A real valued distance measure is... Read More about A fuzzy directional distance measure.

Tuning a multiple classifier system for side effect discovery using genetic algorithms (2014)
Conference Proceeding
Reps, J. M., Aickelin, U., & Garibaldi, J. M. (2014). Tuning a multiple classifier system for side effect discovery using genetic algorithms. In 2014 IEEE Congress on Evolutionary Computation (CEC). https://doi.org/10.1109/CEC.2014.6900328

In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side effects were identified when different binary classifiers were used wi... Read More about Tuning a multiple classifier system for side effect discovery using genetic algorithms.

Data classification using the Dempster-Shafer method (2014)
Journal Article
Chen, Q., Whitbrook, A., Aickelin, U., & Roadknight, C. (2014). Data classification using the Dempster-Shafer method. Journal of Experimental and Theoretical Artificial Intelligence, https://doi.org/10.1080/0952813X.2014.886301

In this paper, the Dempster-Shafer method is employed as the theoretical basis for creating data classification systems. Testing is carried out using three popular (multiple attribute) benchmark datasets that have two, three and four classes. In each... Read More about Data classification using the Dempster-Shafer method.

Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs (2014)
Journal Article
Reps, J., M. Garibaldi, J., Aickelin, U., Soria, D., E. Gibson, J., & B. Hubbard, R. (2014). Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs. Drug Safety, 37(3), 163-170. https://doi.org/10.1007/s40264-014-0137-z

Background: Children are frequently prescribed medication `o-label', meaning there has not been sucient testing of the medication to determine its safety or eectiveness. The main reason this safety knowledge is lacking is due to ethical restrictions... Read More about Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs.

Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer (2014)
Journal Article
Figueredo, G. P., Siebers, P., Owen, M. R., Reps, J., & Aickelin, U. (2014). Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer. PLoS ONE, 9(4), Article e95150. https://doi.org/10.1371/journal.pone.0095150

There is great potential to be explored regarding the use of agent-based modelling and simulation as an alternative paradigm to investigate early-stage cancer interactions with the immune system. It does not suffer from some limitations of ordinary d... Read More about Comparing stochastic differential equations and agent-based modelling and simulation for early-stage cancer.

A data mining framework to model consumer indebtedness with psychological factors (2014)
Conference Proceeding
Ladas, A., Ferguson, E., Garibaldi, J. M., & Aickelin, U. (2014). A data mining framework to model consumer indebtedness with psychological factors.

Modelling Consumer Indebtedness has proven to be a problem of complex nature. In this work we utilise Data Mining techniques and methods to explore the multifaceted aspect of Consumer Indebtedness by examining the contribution of Psychological Factor... Read More about A data mining framework to model consumer indebtedness with psychological factors.

Personalising mobile advertising based on users’ installed apps (2014)
Conference Proceeding
Reps, J., Aickelin, U., Garibaldi, J. M., & Damski, C. (2014). Personalising mobile advertising based on users’ installed apps.

Mobile advertising is a billion pound industry that is rapidly expanding. The success of an advert is measured based on how users interact with it. In this paper we investigate whether the application of unsupervised learning and association rule min... Read More about Personalising mobile advertising based on users’ installed apps.

Incorporating spontaneous reporting system data to aid causal inference in longitudinal healthcare data (2014)
Conference Proceeding
Reps, J. M., & Aickelin, U. (2014). Incorporating spontaneous reporting system data to aid causal inference in longitudinal healthcare data.

Inferring causality using longitudinal observational databases is challenging due to the passive way the data are collected. The majority of associations found within longitudinal observational data are often non-causal and occur due to confounding.... Read More about Incorporating spontaneous reporting system data to aid causal inference in longitudinal healthcare data.

Refining adverse drug reactions using association rule mining for electronic healthcare data (2014)
Conference Proceeding
Reps, J. M., Aickelin, U., Ma, J., & Zhang, Y. (2014). Refining adverse drug reactions using association rule mining for electronic healthcare data.

Side effects of prescribed medications are a common occurrence. Electronic healthcare databases present the opportunity to identify new side effects efficiently but currently the methods are limited due to confounding (i.e. when an association betwee... Read More about Refining adverse drug reactions using association rule mining for electronic healthcare data.

Variability of behaviour in electricity load profile clustering: who does things at the same time each day? (2014)
Conference Proceeding
Dent, I., Craig, T., Aickelin, U., & Rodden, T. (2014). Variability of behaviour in electricity load profile clustering: who does things at the same time each day?. In P. Perner (Ed.), Advances in data mining: applications and theoretical aspects: 14th Industrial Conference, ICDM 2014, St. Petersburg, Russia, July 16-20, 2014: proceedings (70–84). https://doi.org/10.1007/978-3-319-08976-8_6

UK electricity market changes provide opportunities to alter households' electricity usage patterns for the benefit of the overall electricity network. Work on clustering similar households has concentrated on daily load profiles and the variability... Read More about Variability of behaviour in electricity load profile clustering: who does things at the same time each day?.