Dangers of a fixed mindset: implications of self-theories research for computer science education

Murphy, L. and Thomas, L. 2008. Dangers of a fixed mindset: implications of self-theories research for computer science education. In Proceedings of the 13th Annual Conference on innovation and Technology in Computer Science Education (Madrid, Spain, June 30 - July 02, 2008). ITiCSE '08. ACM, New York, NY, 271-275.

Thinking with and outside the box: developing computer support for evidence teaching

B. Schafer, J. Keppens and Q. Shen. Thinking with and outside the Box: Developing Computer Support for Evidence Teaching. P. Robert and M. Redmayne (Eds.), Innovations in Evidence and Proof: Integrating Theory, Research and Teaching, pp. 139-158, 2007.

Strategies that students use to trace code: an analysis based in grounded theory

Fitzgerald, S., Simon, B., and Thomas, L. 2005. Strategies that students use to trace code: an analysis based in grounded theory. In Proceedings of the First international Workshop on Computing Education Research (Seattle, WA, USA, October 01 - 02, 2005). ICER '05. ACM, New York, NY, 69-80

Meta-stable memory in an artificial immune network

Neal, M., Meta-stable memory in an artificial immune network, Proceedings of the 2nd International Conference on Artificial Immune Systems {ICARIS}, Springer, 168-180, 2003,LNCS 2787/2003

Computer-Based Diagnostic Systems: Computer-Based Troubleshooting

ISBN: 3-540-76198-5 (out of print)

Fuzzy-Rough Sets Assisted Attribute Selection

R. Jensen and Q. Shen. Fuzzy-Rough Sets Assisted Attribute Selection. IEEE Transactions on Fuzzy Systems, vol. 15, no. 1, pp. 73-89, 2007.

Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough-Based Approaches

R. Jensen and Q. Shen. Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy-Rough Based Approaches. IEEE Transactions on Knowledge and Data Engineering, 16(12): 1457-1471. 2004.

The evaluation of evidence for exponentially distributed data

C.G.G. Aitken, Q. Shen, R. Jensen and B. Hayes. The evaluation of evidence for exponentially distributed data. Computational Statistics & Data Analysis, vol. 51, no. 12, pp. 5682-5693, 2007.

Knowledge representation for fuzzy model composition

X. Fu and Q. Shen. 'Knowledge representation for fuzzy model composition', in Proceedings of the 21st International Workshop on Qualitative Reasoning, 2007, pp. 47-54. Sponsorship: EPSRC

Rough feature selection for intelligent classifiers

Q. Shen. Rough feature selection for intelligent classifiers. LNCS Transactions on Rough Sets, 7:244-255, 2007.

Rough sets, their extensions and applications

Q. Shen and R. Jensen, 'Rough sets, their extensions and applications,' International Journal of Automation and Computing (IJAC), vol. 4, no. 3, pp. 217-218, 2007.

Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma

X. Wang, J. Yang, R. Jensen and X. Liu, 'Rough Set Feature Selection and Rule Induction for Prediction of Malignancy Degree in Brain Glioma,' Computer Methods and Programs in Biomedicine, vol. 83, no. 2, pp. 147-156, 2006.

Fuzzy-Rough Data Reduction with Ant Colony Optimization.

R. Jensen and Q. Shen, 'Fuzzy-Rough Data Reduction with Ant Colony Optimization,' Fuzzy Sets and Systems, vol. 149, no. 1, pp. 5-20, 2005.

Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring.

Q. Shen and R. Jensen, 'Selecting Informative Features with Fuzzy-Rough Sets and its Application for Complex Systems Monitoring,' Pattern Recognition, vol. 37, no. 7, pp. 1351-1363, 2004.

Rough set based feature selection : A review

Feature selection aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory (RST) has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in a dataset using the data alone, requiring no additional information. This chapter describes the fundamental ideas behind RST-based approaches and reviews related feature selection methods that build on these ideas. Extensions to the traditional rough set approach are discussed, including recent selection methods based on tolerance rough sets, variable precision rough sets and fuzzy-rough sets. Alternative search mechanisms are also highly important in rough set feature selection. The chapter includes the latest developments in this area, including RST strategies based on hill-climbing, genetic algorithms and ant colony optimization.