Multilabel classification is a challenging research problem in which each instance is assigned to a subset of labels. Recently, a considerable amount of research has been concerned...
Muhammad Atif Tahir, Josef Kittler, Krystian Mikol...
Abstract. In many classification problems, and in particular in medical domains, it is common to have an unbalanced class distribution. This pose problems to classifiers as they ...
We suggest a general paradigm of using large-scale distributed computation to solve difficult problems, but where humans can act as agents and provide candidate solutions. We are e...
Craig Gentry, Zulfikar Ramzan, Stuart G. Stubblebi...
Mining discrete patterns in binary data is important for subsampling, compression, and clustering. We consider rankone binary matrix approximations that identify the dominant patt...
Abstract. The paradigm of pattern discovery based on constraints was introduced with the aim of providing to the user a tool to drive the discovery process towards potentially inte...