Algorithm Selection for Classification Problems via Cluster based Meta features
Meta features describe the characteristics of the datasets to facilitate algorithm selection. This paper proposes a new set of meta features based on clustering the instances within datasets. We propose the use of a greedy clustering algorithm, and evaluate the meta features generated based on the learning curves produced by the Random Forest algorithm. We also compared the utility of the proposed meta features against preexisting meta features described in the literature, and evaluated the applicability of these meta features over a sample of UCI datasets. Our results show that these meta features do indeed improve the performance when applied to the algorithm selection task.
2018 IEEE International Conference on Big Data (Big Data)