Machine Learning methods and algorithms are often highly modular in the sense that they rely on a large number of subalgorithms that are in principle interchangeable. For example, it is often possible to use various kinds of pre- and post-processing and various base classifiers or regressors as components of the same modular approach. We propose a framework, called Scavenger, that allows evaluating whole families of conceptually similar algorithms efficiently. The algorithms are represented as compositions, couplings and products of atomic subalgorithms. This allows partial results to be cached and shared between different instances of a modular algorithm, so that potentially expensive partial results need not be recomputed multiple times. Furthermore, our framework deals with issues of the parallel execution, load balancing, and with the backup of partial results for the case of implementation or runtime errors. Scavenger is licensed under the GPLv3 and can be downloaded freely at Github.
Bifet, Albert; May, Michael; Zadrozny, Bianca; Gavalda, Ricard; Pedreschi, Dino; Cardoso, Jaime; Spiliopoulou, Myra (Ed.): Machine Learning and Knowledge Discovery in Databases, pp. 325-328, Springer International Publishing, 2015, ISBN: 978-3-319-23460-1.