Towards integrative machine learning and knowledge extraction

Abstract

This Volume is a result of workshop 15w2181 “Advances in interactive knowledge discovery and data mining in complex and big data sets” at the Banff International Research Station for Mathematical Innovation and Discovery. The workshop was dedicated to bring together experts with diverse backgrounds but with one common goal: to understand intelligence for the successful design, development and evaluation of algorithms that can learn from data, extract knowledge from experience, and to improve their learning behaviour over time – similarly as we humans do. Knowledge discovery, data mining, machine learning, artificial intelligence are more or less synonymously used with no strict definitions or boundaries. “Integrative” means to support not only the machine learning & knowledge extraction pipeline, ranging from dealing with data in arbitrarily high-dimensional spaces to the visualization of results into a lower dimension accessible to a human; it is taking into account seemingly disparate fields which can be very fruitful when brought together - for solving problems in complex application domains (e.g. health informatics). Here we want to emphasize that the most important findings in machine learning will be those we do not know yet. In this paper we provide: (1) a short motivation for the integrative approach; (2) brief summaries of the presentations given in Banff; and (3) some personally flavoured, subjective future research outlooks, e.g. in the combination of geometrical approaches with machine learning.