@inproceedings{kotthoff_hybrid_2012,
  title = {Hybrid {Regression}-{Classification} {Models} for {Algorithm} {Selection}},
  abstract = {Many state of the art Algorithm Selection systems use Machine Learning to either predict the run time or a similar performance measure of each of a set of algorithms and choose the algorithm with the best predicted performance or predict the best algorithm directly. We present a technique based on the well-established Machine Learning technique of stacking that combines the two approaches into a new hybrid approach and predicts the best algorithm based on predicted run times. We demonstrate significant performance improvements of up to a factor of six compared to the previous state of the art. Our approach is widely applicable and does not place any restrictions on the performance measure used, the way to predict it or the Machine Learning used to predict the best algorithm. We investigate different ways of deriving new Machine Learning features from the predicted performance measures and evaluate their effectiveness in increasing performance further. We use five different regression algorithms for performance prediction on five data sets from the literature and present strong empirical evidence that shows the effectiveness of our approach.},
  booktitle = {20th {European} {Conference} on {Artificial} {Intelligence}},
  author = {Kotthoff, Lars},
  month = aug,
  year = {2012},
  pages = {480--485},
  month_numeric = {8}
}