Outputs (76)

EBNO : evolution of cost-sensitive Bayesian networks (2019)
Journal Article
Nashnush, E., & Vadera, S. (2020). EBNO : evolution of cost-sensitive Bayesian networks. Expert Systems, 37(3), e12495. https://doi.org/10.1111/exsy.12495

The last decade has seen an increase in the attention paid to the development of cost sensitive learning algorithms that aim to minimize misclassification costs while still maintaining accuracy. Most of this attention has been on cost sensitive dec... Read More about EBNO : evolution of cost-sensitive Bayesian networks.

Pruning neural networks using multi-armed bandits (2019)
Journal Article
Ameen, S., & Vadera, S. (2020). Pruning neural networks using multi-armed bandits. Computer Journal, 63(7), 1099-1108. https://doi.org/10.1093/comjnl/bxz078

The successful application of deep learning has led to increasing expectations of their use in embedded systems. This in turn, has created the need to find ways of reducing the size of neural networks. Decreasing the size of a neural network requi... Read More about Pruning neural networks using multi-armed bandits.

Natural Language Processing and Information Systems : 24th International Conference on applications of natural language to information systems, NLDB 2019, Salford, UK, June 26–28, 2019, proceedings (2019)
Book
24th International Conference on applications of natural language to information systems, NLDB 2019, Salford, UK, June 26–28, 2019, proceedings. Switzerland: Springer Nature Switzerland. https://doi.org/10.1007/978-3-030-23281-8

This book constitutes the refereed proceedings of the 24th International Conference on Applications of Natural Language to Information Systems, NLDB 2019, held in Salford, UK, in June 2019. The 21 full papers and 16 short papers were carefully re... Read More about Natural Language Processing and Information Systems : 24th International Conference on applications of natural language to information systems, NLDB 2019, Salford, UK, June 26–28, 2019, proceedings.

Case studies in applying data mining for churn analysis (2017)
Journal Article
Lomax, S., & Vadera, S. (2017). Case studies in applying data mining for churn analysis. International Journal of Conceptual Structures and Smart Applications, 5(2), 22-33. https://doi.org/10.4018/ijcssa.2017070102

The advent of price and product comparison sites now makes it even more important to retain customers and identify those that might be at risk of leaving. The use of data mining methods has been widely advocated for predicting customer churn. This pa... Read More about Case studies in applying data mining for churn analysis.

A social norms approach to changing school children’s perceptions of tobacco usage (2017)
Journal Article
Sheikh, A., Vadera, S., Ravey, M., Lovatt, G., & Kelly, G. (2017). A social norms approach to changing school children’s perceptions of tobacco usage. Health Education, 117(6), 530-539. https://doi.org/10.1108/he-01-2017-0006

Purpose: Over 200,000 young people in the UK embark on a smoking career annually, thus continued effort is required to understand the types of interventions that are most effective in changing perceptions about smoking amongst teenagers. Several auth... Read More about A social norms approach to changing school children’s perceptions of tobacco usage.

A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images (2017)
Journal Article
Ameen, S., & Vadera, S. (2017). A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images. Expert Systems, 34(3), e12197. https://doi.org/10.1111/exsy.12197

Sign language is used by approximately 70 million1 people throughout the world, and an automatic tool for interpreting it could make a major impact on communication between those who use it and those who may not understand it. However, computer inte... Read More about A convolutional neural network to classify American Sign Language fingerspelling from depth and colour images.

Learning cost-sensitive Bayesian networks via direct and indirect methods (2016)
Journal Article
Nashnush, E., & Vadera, S. (2017). Learning cost-sensitive Bayesian networks via direct and indirect methods. Integrated Computer-Aided Engineering, 24(1), 17-26. https://doi.org/10.3233/ICA-160514

Cost-Sensitive learning has become an increasingly important area that recognizes that real world classification problems need to take the costs of misclassification and accuracy into account. Much work has been done on cost-sensitive decision tree l... Read More about Learning cost-sensitive Bayesian networks via direct and indirect methods.

Natural language processing and information systems : 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Salford, UK, June 22-24, 2016, Proceedings (2016)
Book
(2016). E. Metais, F. Meziane, M. Saraee, V. Sugumaran, S. Vadera, E. Métais, …S. Vadera (Eds.), Natural language processing and information systems : 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Salford, UK, June 22-24, 2016, Proceedings. Springer. https://doi.org/10.1007/978-3-319-41754-7

This volume of the lecture notes in computer science (LNCS) contains the papers presented at the 21st International Conference on application of Natural Language to Information Systems, held at MediacityUK, University of Salford on the 22-24 June 201... Read More about Natural language processing and information systems : 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Salford, UK, June 22-24, 2016, Proceedings.

A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework (2016)
Journal Article
Lomax, S., & Vadera, S. (2017). A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework. Computer Journal, 60(7), 941-956. https://doi.org/10.1093/comjnl/bxw015

This paper develops a new algorithm for inducing cost-sensitive decision trees that is inspired by the multi-armed bandit problem, in which a player in a casino has to decide which slot machine (bandit) from a selection of slot machines is likely to... Read More about A cost-sensitive decision tree learning algorithm based on a multi-armed bandit framework.

Feature selection in meta learning framework (2014)
Presentation / Conference
Shilbayeh, S., & Vadera, S. (2014, August). Feature selection in meta learning framework. Presented at The Science and Information Conference, Science and Information Conference

Feature selection is a key step in data mining. Unfortunately, there is no single feature selection method that is always the best and the data miner usually has to experiment with different methods using a trial and error approach, which can be time... Read More about Feature selection in meta learning framework.