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All Outputs (61)

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.

Cost-sensitive Bayesian network learning using sampling (2014)
Book Chapter
Nashnush, E., & Vadera, S. (2014). Cost-sensitive Bayesian network learning using sampling. In Recent Advances on Soft Computing and Data Mining (467-476). Springer. https://doi.org/10.1007/978-3-319-07692-8_44

A significant advance in recent years has been the development of cost-sensitive decision tree learners, recognising that real world classification problems need to take account of costs of misclassification and not just focus on accuracy. The litera... Read More about Cost-sensitive Bayesian network learning using sampling.

Natural language processing and information systems : 18th international conference on applications of natural language to information systems, NLDB 2013, Salford, UK, June 2013; Proceedings (2013)
Book
(2013). E. Métais, F. Meziane, M. Saraee, & S. Vadera (Eds.), Natural language processing and information systems : 18th international conference on applications of natural language to information systems, NLDB 2013, Salford, UK, June 2013; Proceedings. Springer

This book constitutes the refereed proceedings of the 18th International Conference on Applications of Natural Language to Information Systems, held in Salford, UK, in June 2013. The 21 long papers, 15 short papers and 17 poster papers presented... Read More about Natural language processing and information systems : 18th international conference on applications of natural language to information systems, NLDB 2013, Salford, UK, June 2013; Proceedings.

A survey of cost-sensitive decision tree induction algorithms (2013)
Journal Article
Lomax, S., & Vadera, S. (2013). A survey of cost-sensitive decision tree induction algorithms. ACM computing surveys, 45(2), 16:1-16:35. https://doi.org/10.1145/2431211.2431215

The past decade has seen a significant interest on the problem of inducing decision trees that take account of costs of misclassification and costs of acquiring the features used for decision making. This survey identifies over 50 algorithms includi... Read More about A survey of cost-sensitive decision tree induction algorithms.

Preface to the workshop on cost sensitive data mining (2012)
Book Chapter
Vadera, S., Saraee, M., & Lomax, S. (2012). Preface to the workshop on cost sensitive data mining. In J. Vreeken, C. Ling, M. Zaki, A. Siebes, J. Yu, B. Goethals, …X. Wu (Eds.), The 12th IEEE International Conference on Data Mining : Workshops. IEEE. https://doi.org/10.1109/ICDMW.2012.148

Much of the early work on data mining concentrated on developing algorithms that focused on classification accuracy. A more challenging and practical problem is to devise algorithms that learn rules or associations that optimize income and take bette... Read More about Preface to the workshop on cost sensitive data mining.

A multi-armed bandit approach to cost-sensitive decision tree learning (2012)
Presentation / Conference
Lomax, S., Vadera, S., & Saraee, M. (2012, December). A multi-armed bandit approach to cost-sensitive decision tree learning. Presented at 2012 IEEE 12th International Conference on Data Mining Workshops, Brussels, Belgium

Several authors have studied the problem of inducing decision trees that aim to minimize costs of misclassification and take account of costs of tests. The approaches adopted vary from modifying the information theoretic attribute selection measure u... Read More about A multi-armed bandit approach to cost-sensitive decision tree learning.

An empirical comparison of cost-sensitive decision tree induction algorithms (2011)
Journal Article
Lomax, S., & Vadera, S. (2011). An empirical comparison of cost-sensitive decision tree induction algorithms. Expert Systems, 28(3), 227-268. https://doi.org/10.1111/j.1468-0394.2010.00573.x

Decision tree induction is a widely used technique for learning from data which first emerged in the 1980s. In recent years, several authors have noted that in practice, accuracy alone is not adequate, and it has become increasingly important to tak... Read More about An empirical comparison of cost-sensitive decision tree induction algorithms.

A survey of AI in operations management from 2005 to 2009 (2011)
Journal Article
Kobbacy, K., & Vadera, S. (2011). A survey of AI in operations management from 2005 to 2009. Journal of Manufacturing Technology Management, 22(6), 706-733. https://doi.org/10.1108/17410381111149602

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it c... Read More about A survey of AI in operations management from 2005 to 2009.

CSNL: A cost-sensitive non-linear decision tree algorithm (2010)
Journal Article
Vadera, S. (2010). CSNL: A cost-sensitive non-linear decision tree algorithm. ACM transactions on knowledge discovery from data, 4(2), 1-25. https://doi.org/10.1145/1754428.1754429

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision... Read More about CSNL: A cost-sensitive non-linear decision tree algorithm.

Intelligent information processing V (2010)
Book
Leake, D., Muggleton, S., Shawe-Taylor, J., Shi, Z., Chen, L., & Cai, G. (2010). Z. Shi, S. Vadera, A. Aamodt, & D. Leake (Eds.), Intelligent information processing V. Berlin, Heidelberg, New York: Springer

This volume comprises of selected papers from the 6th IFIP International Conference on Intelligent Information Processing. As the world proceeds quickly into the Information Age, it encounters both successes and challenges, and it is well recognized... Read More about Intelligent information processing V.

Artificial intelligence applications for improved software engineering development : new prospects (2009)
Book
Meziane, F., & Vadera, S. (2009). Artificial intelligence applications for improved software engineering development : new prospects. Hershey, New York, USA: IGI Global

Despite decades of research, developing software that is fit for purpose, developed on time, and within budget remains a challenge. Many researchers have advocated the use of artificial intelligence techniques such as knowledge-based systems, neural... Read More about Artificial intelligence applications for improved software engineering development : new prospects.

AI in software engineering : current developments and future prospects (2009)
Book Chapter
Meziane, F., & Vadera, S. (2009). AI in software engineering : current developments and future prospects. In F. Meziane, & S. Vadera (Eds.), Artificial Intelligence Applications for Improved Software Engineering Development: New Prospects (273-294). Hershey, New York, USA: IGI Global

Artificial intelligences techniques such as knowledge based systems, neural networks, fuzzy logic and data mining have been advocated by many researchers and developers as the way to improve many of the software development activities. As with many o... Read More about AI in software engineering : current developments and future prospects.

Informatics Research Institute (IRIS) September 2008 newsletter (2008)
Other
Vadera, S. (2008). Informatics Research Institute (IRIS) September 2008 newsletter. Salford, UK

2007-8 was a very busy year for IRIS. It was a bumper year for visiting Profs with Prof Michael Myers visiting from New Zealand, Prof Brian Fitzgerald visiting from University of Limerick, Ireland, Prof. Uzay Kaymak visiting from Erasmus University N... Read More about Informatics Research Institute (IRIS) September 2008 newsletter.

Using Wittgenstein’s family resemblance principle to learn exemplars (2008)
Journal Article
Vadera, S., Rodriguez, A., Succar, E., & Wu, J. (2008). Using Wittgenstein’s family resemblance principle to learn exemplars. Foundations of Science, 13(1), 67-74. https://doi.org/10.1007/s10699-007-9119-2

The introduction of the notion of family resemblance represented a major shift in Wittgenstein’s thoughts on the meaning of words, moving away from a belief that words were well defined, to a view that words denoted less well defined categories of... Read More about Using Wittgenstein’s family resemblance principle to learn exemplars.

AI and OR in management of operations: history and trends (2007)
Journal Article
Kobbacy, K., Vadera, S., & Rasmy, M. (2007). AI and OR in management of operations: history and trends. Journal of the Operational Research Society, 58(1), 10-28. https://doi.org/10.1057/palgrave.jors.2602132

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context fo... Read More about AI and OR in management of operations: history and trends.

A probabilistic model for information and sensor validation (2006)
Journal Article
Ibargüengoytia, P., Vadera, S., & Sucar, L. (2006). A probabilistic model for information and sensor validation. Computer Journal, 49(1), 113-126. https://doi.org/10.1093/comjnl/bxh142

This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the... Read More about A probabilistic model for information and sensor validation.

Inducing safer oblique trees without costs (2005)
Journal Article
Vadera, S. (2005). Inducing safer oblique trees without costs. Expert Systems, 22(4), 206-221. https://doi.org/10.1111/j.1468-0394.2005.00311.x

Decision tree induction has been widely studied and applied. In safety applications, such as determining whether a chemical process is safe or whether a person has a medical condition, the cost of misclassification in one of the classes is significan... Read More about Inducing safer oblique trees without costs.

Obtaining E-R diagrams semi-automatically from natural language specifications (2004)
Presentation / Conference
Meziane, F., & Vadera, S. (2004, April). Obtaining E-R diagrams semi-automatically from natural language specifications. Poster presented at Sixth International Conference on Enterprise Information Systems (ICEIS 2004), Universidade Portucalense, Porto, Portugal

Since their inception, entity relationship models have played a central role in systems specification, analysis and development. They have become an important part of several development methodologies and standards such as SSADM. Obtaining entity r... Read More about Obtaining E-R diagrams semi-automatically from natural language specifications.