Outputs (76)

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.

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.