EML: A Scalable, Transparent Meta-Learning Paradigm for Big Data Applications

The work presented in this chapter is motivated by two important challenges that arise when applying ML techniques to big data applications: the scalability of an ML technique as the training data increases significantly in size, and the transparency (understandability) of the induced models. To address these issues we describe and analyze a meta-learning paradigm, EML, that combines techniques from evolutionary computation and supervised learning to produce a powerful approach for inducing transparent models for big data ML applications.