Using meta-learning for model type selection in predictive big data analytics

One of the biggest challenges for today's data scientists is to be able to make an informed decision among an exhaustive number of different modeling techniques. As no single algorithm can perform optimally in all cases, the context for the modeling task including the dataset characteristics plays an unsurprisingly important role in deciding which modeling algorithm to choose. In our previous work, we have presented an ontology-based automated model-selection system extending the Scala-based SCALATION data framework. In this study, we present a meta-learning based model-selection system and provide an evaluation of the system.