Entropy 2016 - Special Issue "Machine Learning and Entropy: Discover Unknown Unknowns in Complex Data Sets"

In the real world, we are confronted, not only with complex and high-dimensional data sets, but also usually with noisy, incomplete, and uncertain data, where the application of traditional methods of knowledge discovery and data mining always entail the danger of modeling artifacts. Originally, information entropy was introduced by Shannon (1949), as a measure of uncertainty in data. Up to the present, many different types of entropy methods with a large number of different purposes and possible application areas have emerged. In this Special Issue we are seeking papers discussing advances in the application of learning algorithms and entropy for use in knowledge discovery and data mining, to discover unknowns in complex data sets, e.g., for biomarker discovery in biomedical data sets.