Scavenger : a framework for efficient evaluation of dynamic and modular algorithms
Bifet, Albert (Hrsg). Machine learning and knowledge discovery in databases : European conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015; proceedings. Pt. 3. Cham: Springer 2015 S. 325 - 328
Erscheinungsjahr: 2015
ISBN/ISSN: 978-3-319-23460-1 ; 3-319-23460-9
Publikationstyp: Buchbeitrag
Sprache: Englisch
Doi/URN: 10.1007/978-3-319-23461-8_40
Geprüft | Bibliothek |
Inhaltszusammenfassung
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 a...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 https://github.com/jorro/scavenger.» weiterlesen» einklappen
Autoren
Klassifikation
DFG Fachgebiet:
Informatik
DDC Sachgruppe:
Informatik