Some Thoughts at the Interface of Ensemble Methods and Feature Selection

Abstract

We are in a very exciting time for Machine Learning. The field is making its first steps toward true industrial-strength technology, slowly transitioning from a disparate collection of techniques, to a mature science. Multiple Classifier Systems in particular, are showing repeated successes at the most competitive of levels: winning the Netflix challenge, forming the backbone of cutting edge real-time computer vision, and most recently steering Google’s interests in quantum algorithms. It is thus becoming more and more difficult to generate truly meaningful contributions with our research. In the context of multiple classifier systems, we must ask ourselves, “how can we generate new MCS research that is truly meaningful?”