| Full text | |
| Author(s): |
Alcobaca, Edesio
[1]
;
Siqueira, Felipe
[1]
;
Rivolli, Adriano
[1]
;
Garcia, Luis P. F.
[1]
;
Oliva, Jefferson T.
[1]
;
de Carvalho, Andre C. P. L. F.
[1]
Total Authors: 6
|
| Affiliation: | [1] Univ Sao Paulo, Inst Math & Comp Sci, Av Trabalhador Sao Carlense 400, BR-13560970 Sao Carlos, SP - Brazil
Total Affiliations: 1
|
| Document type: | Journal article |
| Source: | JOURNAL OF MACHINE LEARNING RESEARCH; v. 21, 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Automated recommendation of machine learning algorithms is receiving a large deal of attention, not only because they can recommend the most suitable algorithms for a new task, but also because they can support efficient hyper-parameter tuning, leading to better machine learning solutions. The automated recommendation can be implemented using meta-learning, learning from previous learning experiences, to create a meta-model able to associate a data set to the predictive performance of machine learning algorithms. Although a large number of publications report the use of meta-learning, reproduction and comparison of meta-learning experiments is a difficult task. The literature lacks extensive and comprehensive public tools that enable the reproducible investigation of the different meta-learning approaches. An alternative to deal with this difficulty is to develop a meta-feature extractor package with the main characterization measures, following uniform guidelines that facilitate the use and inclusion of new meta-features. In this paper, we propose two Meta-Feature Extractor (MFE) packages, written in both Python and R, to fill this lack. The packages follow recent frameworks for meta-feature extraction, aiming to facilitate the reproducibility of meta-learning experiments. (AU) | |
| FAPESP's process: | 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry |
| Grantee: | Francisco Louzada Neto |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 18/14819-5 - Automated machine learning: learning to learn |
| Grantee: | Edesio Pinto de Souza Alcobaça Neto |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |
| FAPESP's process: | 16/18615-0 - Advanced machine learning |
| Grantee: | André Carlos Ponce de Leon Ferreira de Carvalho |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |