Aiding classification of gene expression data with feature selection: a comparative study

C. Shang and Q. Shen. Aiding classification of gene expression data with feature selection: a comparative study. Computational Intelligence Research, 1(1):68-76.

Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics

Struyf, J., Dzeroski, S. Blockeel, H. and Clare, A. (2005) Hierarchical Multi-classification with Predictive Clustering Trees in Functional Genomics. In proceedings of the EPIA 2005 CMB Workshop

Model Selection Methodology in Supervised Learning with Evolutionary Computation

Rowland, J.J. (2003) Model Selection Methodology in Supervised Learning with Evolutionary Computation. BioSystems 72, 1-2, pp 187-196, Nov

Generalisation and Model Selection in Supervised Learning with Evolutionary Computation

Rowland, J. J. (2003) Generalisation and Model Selection in Supervised Learning with Evolutionary Computation. European Workshop on Evolutionary Computation in Bioinformatics: EvoBio 2003. Lecture Notes in Computer Science (Springer), Vol 2611, pp 119-130

Machine learning of functional class from phenotype data

Clare, A. and King R.D. (2002) Machine learning of functional class from phenotype data. Bioinformatics 18(1) 160-166

Webpage Classification with ACO-enhanced Fuzzy-Rough Feature Selection.

R. Jensen and Q. Shen, 'Webpage Classification with ACO-enhanced Fuzzy-Rough Feature Selection,' Proceedings of the Fifth International Conference on Rough Sets and Current Trends in Computing (RSCTC 2006), LNAI 4259, pp. 147-156, 2006.

Evolutionary approaches to fuzzy modelling for classification

M. Galea, Q. Shen and J. Levine. Evolutionary approaches to fuzzy modelling. Knowledge Engineering Review, 19(1):27-59, 2004.

Subsethood-based fuzzy modelling and classification

K. Rasmani and Q. Shen. Subsethood-based fuzzy modelling and classification. Proceedings of the 2004 UK Workshop on Computational Intelligence, pages 181-188.

A novel breast tissue density classification framework

Oliver, A., Freixenet, J., Marti, R., Pont, J., Perez, E., Denton, E. R. E., Zwiggelaar, R. (2008). A novel breast tissue density classification framework. IEEE Transactions on Information Technology in BioMedicine, 12 (1), 55-65

Linear structures in mammographic images: detection and classification

R. Zwiggelaar, S.M. Astley, C.J. Taylor and C.R.M. Boggis, 'Linear structures in mammographic images: detection and classification', IEEE Transaction on Medical Imaging 23 (9), 1077-1086 (2004)

Report for the British Board of Film Classification on viewers of the Lord of the Rings, aged under 16

This is a report on what can be learnt from our world dataset about viewers of The Lord of the Rings who were aged under 16. In this report, I draw both on the world set, and on the UK subset, sometimes drawing comparisons between them. The reason for using both is that, obviously, the world set is so much larger (comprising 24,739 in toto, with 2475 under 16), but the UK set (comprising 3115 in toto, and 306 under 16s) allows us to explore both some of the specificities of responses here, the qualitative meaning of some responses (given we worked in 14 languages, many are inaccessible to us for analysis), and of course their relations to the quantitative patterns that emerge.

Semi-Supervised OWA Aggregation for Link-Based Similarity Evaluation and Alias Detection

T.Boongoen and Q. Shen. Semi-Supervised OWA Aggregation for Link-Based Similarity Evaluation and Alias Detection. Proceedings of the 18th International Conference on Fuzzy Systems (FUZZ-IEEE'09), pp. 288-293, 2009. Sponsorship: EPSRC