Algorithm selection via meta-learning and sample-based active testing

• Type: Article in International Conference Proceedings Book
• Date: 2015

Authors

Abdulrahman, SM (Author) Other; Pavel Brazdil (Author) FEP; Van Rijn, JN (Author) Other; Vanschoren, J (Author) Other

Conference proceedings International

Title: CEUR Workshop Proceedings

Pages: 55-66

International Workshop on Meta-Learning and Algorithm Selection, MetaSel 2015 - co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2015, ECMLPKDD 2015

7 September 2015

Indexing

Scopus - 9 Citations

Other information

Authenticus ID: P-00G-QJ3

Abstract (EN): Identifying the best machine learning algorithm for a given problem continues to be an active area of research. In this paper we present a new method which exploits both meta-level information acquired in past experiments and active testing, an algorithm selection strategy. Active testing attempts to iteratively identify an algorithm whose performance will most likely exceed the performance of previously tried algorithms. The novel method described in this paper uses tests on smaller data sample to rank the most promising candidates, thus optimizing the schedule of experiments to be carried out. The experimental results show that this approach leads to considerably faster algorithm selection.

Language: English

Type (Professor's evaluation): Scientific