All Outputs (4)

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.

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.

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.

A layered any time approach to sensor validation (1997)
Book Chapter
Ibarguengoyatia, P., Vadera, S., & Sucar, E. (1997). A layered any time approach to sensor validation. In Lecture Notes in AI. Springer

Sensors are the most usual source of information in many automatic systems such as automatic control These computerised systems utilise different models of the process being served which usually assume the value of the variables as a correct read... Read More about A layered any time approach to sensor validation.