On Clonal Selection.

Clonal selection has been a dominant theme in many immune-inspired algorithms applied to machine learning and optimisation. We examine existing clonal selections algorithms for learning from a theoertical and empirical perspective and assert that the widely accepted computational interpretation of clonal selection is compromised both algorithmically andbiologically. We suggest a more capable abstraction of the clonal selection principle grounded in probabilistic estimation and approximation and demonstrate how it addresses some of the shortcomings in existing algorithms. We further show that by recasting black-box optimisation as a learning problem, the same abstraction may be re-employed; thereby taking steps toward unifying the clonal selection principle and distinguishing it from natural selection.

Freedom to learn: a radically revised pedagogy to facilitate lifewide learning in the academic curriculum.

Higher education has progressed fairly steadily to a common pedagogical approach which centres on the idea of alignment. In this arrangement, intended learning outcomes are identified and declared; learning activities which will enable the desired learning and development to be achieved are conceived and undertaken with the support of appropriate and effective teaching; and assessment which calls for these outcomes is (ideally) carefully designed and implemented. All three elements are aligned in advance. The same principles and practices underpinned by notions of alignment have been applied to date in most of the purposeful schemes for personal development planning. In this chapter I argue that lifewide learning, wherein learning and development often occur incidentally in multiple and varied real-world situations throughout an individual’s life course, calls for a different approach, and a different pedagogy. Higher education should therefore visualise lifewide learning as an emergent phenomenon wherein the outcomes of learning emerge later on, and are often unintended. Consequently, they cannot be defined in advance of the activities through which they are formed. The main purpose of this chapter is to offer some practical ideas to support the development of pedagogies that would enable programme designers to embed in their programmes the principle and practice of lifewide education.

Using Social Media Content for Screening in Recruitment and Selection: Pros and Cons.

The article considers the arguments that have been made in defence of social media screening as well as issues that arise and may effectively erode the reliability and utility of such data for employers. First, the authors consider existing legal frameworks and guidelines that exist in the UK and the USA, as well as the subsequent ethical concerns that arise when employers access and use social networking content for employment purposes. Second, several arguments in favour of the use of social networking content are made, each of which is considered from several angles, including concerns about impression management, bias and discrimination, data protection and security. Ultimately, the current state of knowledge does not provide a definite answer as to whether information from social networks is helpful in recruitment and selection.

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

E-books in academic libraries: an international overview

Tedd, L. A. (2005). E-books in academic libraries: an international overview. New Review of Academic Librarianship, 11(1), 57-79.

Trends in use of electronic journals in higher education in the UK - views of academic staff and students

Bonthron, Karen; Urquhart, Christine; Thomas, Rhian; Armstrong, Chris; Ellis, David; Everitt, Jean; Fenton, Roger; Lonsdale, Ray; McDermott, Elizabeth; Morris, Helen; Phillips, Rebecca; Spink, Sian, and Yeoman, Alison. (2003, June). Trends in use of electronic journals in higher education in the UK - views of academic staff and students. D-Lib Magazine, 9(6). Retrieved September 8, 2006 from http://www.dlib.org/dlib/june03/urquhart/06urquhart.html This item is freely available online at http://www.dlib.org/dlib/june03/urquhart/06urquhart.html Sponsorship: JISC

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.

Feature Selection based on Rough Sets and Particle Swarm Optimization

X. Wang, J. Yang, X. Teng, W. Xia, and R. Jensen. Feature Selection based on Rough Sets and Particle Swarm Optimization. Pattern Recognition Letters, vol. 28, no. 4, pp. 459-471, 2007.

Rough feature selection for intelligent classifiers

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

Performing Feature Selection with ACO

R. Jensen, 'Performing Feature Selection with ACO. Swarm Intelligence and Data Mining,' A. Abraham, C. Grosan and V. Ramos (eds.), Studies in Computational Intelligence, vol. 34, pp. 45-73. 2006.

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.

Tolerance-based and Fuzzy-Rough Feature Selection.

R. Jensen and Q. Shen, 'Tolerance-based and Fuzzy-Rough Feature Selection,' Proceedings of the 16th International Conference on Fuzzy Systems (FUZZ-IEEE'07), pp. 877-882, 2007.