On a few recent developments in Meta-Learning
     for Algorithm Ranking and Selection

                            Bernhard Pfahringer

                 Waikato University, Hamilton, New Zealand,
                        bernhard@cs.waikato.ac.nz



   Abstract. This talk has two main parts. The first part will focus on
   the use of pair-wise meta-rules for algorithm ranking and selection. Such
   rules can provide interesting insights on their own, but they are also very
   valuable features for more sophisticated schemes like Random Forests. A
   hierarchical variant is able to address complexity issues when the number
   of algorithms to compare is substantial.

   The second part of the talk will focus on meta-learning for data streams,
   which is a very active area of research currently. Stream algorithms need
   to be incremental, and be able to adapt to change in the distribution of
   the data. This poses new challenges for meta-learning.