On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier's structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

Evidence-based psychological therapies: from bench to bedside

Patients with mental health difficulties do not always receive appropriate and recommended psychological treatment for their difficulties, and clinicians are not always appropriately trained to deliver them. This paper considers why this might be the case and provides an overview of the Charlie Waller Institute, a not-for-profit organisation funded by the NHS, University of Reading, and the Charlie Waller Memorial Trust. The Institute seeks to address this problem by training clinicians in wide variety of evidence-based therapies and assessing the impact of this training on clinician knowledge and skill.

Molecular dynamics simulation insight into the mechanism of phenol adsorption at low coverages from aqueous solutions on microporous carbons

MD simulation studies showing the influence of porosity and carbon surface oxidation on phenol adsorption from aqueous solutions on carbons are reported. Based on a realistic model of activated carbon, three carbon structures with gradually changed microporosity were created. Next, a different number of surface oxygen groups was introduced. The pores with diameters around 0.6 nm are optimal for phenol adsorption and after the introduction of surface oxygen functionalities, adsorption of phenol decreases (in accordance with experimental data) for all studied models. This decrease is caused by a pore blocking effect due to the saturation of surface oxygen groups by highly hydrogen-bounded water molecules.

An improvement in clear-air turbulence forecasting based on spontaneous imbalance theory: the ULTURB algorithm

Recent research has shown that Lighthill–Ford spontaneous gravity wave generation theory, when applied to numerical model data, can help predict areas of clear-air turbulence. It is hypothesized that this is the case because spontaneously generated atmospheric gravity waves may initiate turbulence by locally modifying the stability and wind shear. As an improvement on the original research, this paper describes the creation of an ‘operational’ algorithm (ULTURB) with three modifications to the original method: (1) extending the altitude range for which the method is effective downward to the top of the boundary layer, (2) adding turbulent kinetic energy production from the environment to the locally produced turbulent kinetic energy production, and, (3) transforming turbulent kinetic energy dissipation to eddy dissipation rate, the turbulence metric becoming the worldwide ‘standard’. In a comparison of ULTURB with the original method and with the Graphical Turbulence Guidance second version (GTG2) automated procedure for forecasting mid- and upper-level aircraft turbulence ULTURB performed better for all turbulence intensities. Since ULTURB, unlike GTG2, is founded on a self-consistent dynamical theory, it may offer forecasters better insight into the causes of the clear-air turbulence and may ultimately enhance its predictability.

An Examination of the factors that shape the subjectivities of female art students in Initial Teacher Training

The school subject of Art and the profession of the primary school teacher are gendered female and both are considered low status within the field of Education and other professional areas of society. A number of sociological studies have examined the impact of gendered socialisation and habitus on females’ career choices and various educational initiatives have been put in place over the years to encourage females to select subjects and/or pursue career paths normally associated with males. Yet Art and primary school teaching continue to be a popular choice with middle class girls. Based on a critical ethnographic study of female BAED Art students, who are training to be primary school teachers, this study is an examination of the many factors, historically and contemporaneously that have shaped and continue to shape the subjectivities of females and frame their aspirations and ambitions. Within this discourse significant aspects of the history of Art and Art Education that have contributed to and influenced the construction of the female artist, and their consequent impact on artistically talented females’ personal identity as artists, are also examined.

Improving insight and non-insight problem solving with brief interventions

Developing brief training interventions that benefit different forms of problem solving is challenging. In earlier research, Chrysikou (2006) showed that engaging in a task requiring generation of alternative uses of common objects improved subsequent insight problem solving. These benefits were attributed to a form of implicit transfer of processing involving enhanced construction of impromptu, on-the-spot or ‘ad hoc’ goal-directed categorizations of the problem elements. Following this, it is predicted that the alternative uses exercise should benefit abilities that govern goal-directed behaviour, such as fluid intelligence and executive functions. Similarly, an indirect intervention – self-affirmation (SA) – that has been shown to enhance cognitive and executive performance after self-regulation challenge and when under stereotype threat, may also increase adaptive goal-directed thinking and likewise should bolster problem-solving performance. In Experiment 1, brief single-session interventions, involving either alternative uses generation or SA, significantly enhanced both subsequent insight and visual–spatial fluid reasoning problem solving. In Experiment 2, we replicated the finding of benefits of both alternative uses generation and SA on subsequent insight problem-solving performance, and demonstrated that the underlying mechanism likely involves improved executive functioning. Even brief cognitive– and social–psychological interventions may substantially bolster different types of problem solving and may exert largely similar facilitatory effects on goal-directed behaviours.

Training pharmacists to deliver a complex information technology intervention (PINCER) using the principles of educational outreach and root cause analysis

Objective: To describe the training undertaken by pharmacists employed in a pharmacist-led information technology-based intervention study to reduce medication errors in primary care (PINCER Trial), evaluate pharmacists’ assessment of the training, and the time implications of undertaking the training. Methods: Six pharmacists received training, which included training on root cause analysis and educational outreach, to enable them to deliver the PINCER Trial intervention. This was evaluated using self-report questionnaires at the end of each training session. The time taken to complete each session was recorded. Data from the evaluation forms were entered onto a Microsoft Excel spreadsheet, independently checked and the summary of results further verified. Frequencies were calculated for responses to the three-point Likert scale questions. Free-text comments from the evaluation forms and pharmacists’ diaries were analysed thematically. Key findings: All six pharmacists received 22 hours of training over five sessions. In four out of the five sessions, the pharmacists who completed an evaluation form (27 out of 30 were completed) stated they were satisfied or very satisfied with the various elements of the training package. Analysis of free-text comments and the pharmacists’ diaries showed that the principles of root cause analysis and educational outreach were viewed as useful tools to help pharmacists conduct pharmaceutical interventions in both the study and other pharmacy roles that they undertook. The opportunity to undertake role play was a valuable part of the training received. Conclusions: Findings presented in this paper suggest that providing the PINCER pharmacists with training in root cause analysis and educational outreach contributed to the successful delivery of PINCER interventions and could potentially be utilised by other pharmacists based in general practice to deliver pharmaceutical interventions to improve patient safety.

The impact of overseas training on curriculum innovation and change in English language education in Western China

This article assesses the impact of a UK-based professional development programme on curriculum innovation and change in English Language Education (ELE) in Western China. Based on interviews, focus group discussions and observation of a total of 48 English teachers who had participated in an overseas professional development programme influenced by modern approaches to education and ELE, and 9 of their colleagues who had not taken part, it assesses the uptake of new approaches on teachers’ return to China. Interviews with 10 senior managers provided supplementary data. Using Diffusion of Innovations Theory as the conceptual framework, we examine those aspects of the Chinese situation that are supportive of change and those that constrain innovation. We offer evidence of innovation in classroom practice on the part of returnees and ‘reinvention’ of the innovation to ensure a better fit with local needs. The key role of course participants as opinion leaders in the diffusion of new ideas is also explored. We conclude that the selective uptake of this innovation is under way and likely to be sustained against a background of continued curriculum reform in China.

The role of rules-based compliance systems in the new EU regulatory landscape: perspectives of institutional change and agency

The financial crisis of 2007-2009 and the subsequent reaction of the G20 have created a new global regulatory landscape. Within the EU, change of regulatory institutions is ongoing. The research objective of this study is to understand how institutional changes to the EU regulatory landscape may affect corresponding institutionalized operational practices within financial organizations and to understand the role of agency within this process. Our motivation is to provide insight into these changes from an operational management perspective, as well as to test Thelen and Mahoney?s (2010) modes of institutional change. Consequently, the study researched implementations of an Investment Management System with a rules-based compliance module within financial organizations. The research consulted compliance and risk managers, as well as systems experts. The study suggests that prescriptive regulations are likely to create isomorphic configurations of rules-based compliance systems, which consequently will enable the institutionalization of associated compliance practices. The study reveals the ability of some agents within financial organizations to control the impact of regulatory institutions, not directly, but through the systems and processes they adopt to meet requirements. Furthermore, the research highlights the boundaries and relationships between each mode of change as future avenues of research.

PDFOS: PDF estimation based over-sampling for imbalanced two-class problems

This contribution proposes a novel probability density function (PDF) estimation based over-sampling (PDFOS) approach for two-class imbalanced classification problems. The classical Parzen-window kernel function is adopted to estimate the PDF of the positive class. Then according to the estimated PDF, synthetic instances are generated as the additional training data. The essential concept is to re-balance the class distribution of the original imbalanced data set under the principle that synthetic data sample follows the same statistical properties. Based on the over-sampled training data, the radial basis function (RBF) classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier’s structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed PDFOS approach is demonstrated by the empirical study on several imbalanced data sets.

Unified framework for construction of rule based classification systems

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfit-ting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances. This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suit-able structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations on Big Data: rule generation, rule simplification and rule representation. The authors also review some existing methods and techniques used for each of the three operations and highlight their limitations. They introduce some novel methods and techniques developed by them recently. These methods and techniques are also discussed in comparison to existing ones with respect to efficient processing of Big Data.

The role of rules-based compliance systems in the new EU regulatory landscape: perspectives of institutional change

Purpose The research objective of this study is to understand how institutional changes to the EU regulatory landscape may affect corresponding institutionalized operational practices within financial organizations. Design/methodology/approach The study adopts an Investment Management System as its case and investigates different implementations of this system within eight financial organizations, predominantly focused on investment banking and asset management activities within capital markets. At the systems vendor site, senior systems consultants and client relationship managers were interviewed. Within the financial organizations, compliance, risk and systems experts were interviewed. Findings The study empirically tests modes of institutional change. Displacement and Layering were found to be the most prevalent modes. However, the study highlights how the outcomes of Displacement and Drift may be similar in effect as both modes may cause compliance gaps. The research highlights how changes in regulations may create gaps in systems and processes which, in the short term, need to be plugged by manual processes. Practical implications Vendors abilities to manage institutional change caused by Drift, Displacement, Layering and Conversion and their ability to efficiently and quickly translate institutional variables into structured systems has the power to ease the pain and cost of compliance as well as reducing the risk of breeches by reducing the need for interim manual systems. Originality/value The study makes a contribution by applying recent theoretical concepts of institutional change to the topic of regulatory change uses this analysis to provide insight into the effects of this new environment

Smart-phone based electrocardiogram wavelet decomposition and neural network classification

This paper discusses ECG classification after parametrizing the ECG waveforms in the wavelet domain. The aim of the work is to develop an accurate classification algorithm that can be used to diagnose cardiac beat abnormalities detected using a mobile platform such as smart-phones. Continuous time recurrent neural network classifiers are considered for this task. Records from the European ST-T Database are decomposed in the wavelet domain using discrete wavelet transform (DWT) filter banks and the resulting DWT coefficients are filtered and used as inputs for training the neural network classifier. Advantages of the proposed methodology are the reduced memory requirement for the signals which is of relevance to mobile applications as well as an improvement in the ability of the neural network in its generalization ability due to the more parsimonious representation of the signal to its inputs.

Youth unemployment and vocational training

This paper focuses on the determinants of the labor market situation of young people in developed countries and the developing world, with a particular emphasis on the role of vocational training and education policies. We highlight the role of demographic factors, economic growth and labor market institutions in explaining young people's transition into work. Subsequently, we assess differences between the setup and functioning of the vocational education and training policies across major world regions as an important driver of differential labor market situation of youth. Based on our analysis, we argue in favor of vocational education and training systems combining work experience and general education and provide some policy recommendations regarding the implementation of education and training systems adapted to a country's economic and institutional context.