All Outputs (3)

A fine-grained Random Forests using class decomposition: an application to medical diagnosis. (2015)
Journal Article
ELYAN, E. and GABER, M.M. 2015. A fine-grained Random Forests using class decomposition: an application to medical diagnosis. Neural computing and applications [online], 27(8), pages 2279-2288. Available from: https://doi.org/10.1007/s00521-015-2064-z

Class decomposition describes the process of segmenting each class into a number of homogeneous subclasses. This can be naturally achieved through clustering. Utilising class decomposition can provide a number of benefits to supervised learning, espe... Read More about A fine-grained Random Forests using class decomposition: an application to medical diagnosis..

On the relationship between variational level set-based and SOM-based active contours. (2015)
Journal Article
ABDELSAMEA, M.M., GNECCO, G., GABER, M.M. and ELYAN, E. 2015. On the relationship between variational level set-based and SOM-based active contours. Computational intelligence and neuroscience [online], 2015, article ID 109029. Available from:https://doi.org/10.1155/2015/109029

Most Active Contour Models (ACMs) deal with the image segmentation problem as a functional optimization problem, as they work on dividing an image into several regions by optimizing a suitable functional. Among ACMs, variational level set methods hav... Read More about On the relationship between variational level set-based and SOM-based active contours..

A scalable expressive ensemble learning using random prism: a MapReduce approach. (2015)
Book Chapter
STAHL, F., MAY, D., MILLS, H., BRAMER, M. and GABER, M.M. 2015. A scalable expressive ensemble learning using random prism: a MapReduce approach. In Hameurlain, A., Küng, J., Wagner, R., Sakr, S., Wang, L. and Zomaya, A. (eds.) Transactions on large-scale data- and knowledge-centred systems XX: special issue on advanced techniques for big data management. Lecture notes in computer science, 9070. Berlin: Springer [online], pages 90-107. Available from: https://doi.org/10.1007/978-3-662-46703-9_4

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the influence of noise in the data, ensemble learners are often app... Read More about A scalable expressive ensemble learning using random prism: a MapReduce approach..