We introduce a method to deal with the problem of learning from imbalanced data sets, where examples of one class significantly outnumber examples of other classes. Our method sel...
When solving machine learning problems, there is currently little automated support for easily experimenting with alternative statistical models or solution strategies. This is be...
Sooraj Bhat, Ashish Agarwal, Alexander Gray, Richa...
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Abstract: Democratic societies throughout the world, it appears, are facing a new type of threat dubbed "asymmetric threat." In this new threat environment the world gove...
In this article, a novel concept is introduced by using both unsupervised and supervised learning. For unsupervised learning, the problem of fuzzy clustering in microarray data as ...