Building algorithm profiles for prior model selection in knowledge discovery systems

We propose the use of learning algorithm profiles to address the model selection problem in knowledge discovery systems. These profiles consist of metalevel feature-value vectors which describe learning algorithms from the point of view of their representation and functionality, efficiency, robustness and practicality. Values for these features are assigned on the basis of author specifications, expert consensus or previous empirical studies. We review past evaluations of the better known learning algorithms and suggest an experimental strategy for building algorithm profiles on more quantitative grounds. Preliminary experiments have disconfirmed expert judgments on certain algorithm features, thus showing the need to build and refine such profiles via controlled experiments