MFE: Towards reproducible meta-feature extraction

Autor(es)

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]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Math & Comp Sci, Av Trabalhador Sao Carlense 400, BR-13560970 Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF MACHINE LEARNING RESEARCH; v. 21, 2020.
• Citações Web of Science: 0

Resumo

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)

• Processo FAPESP: 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria
• Beneficiário: Francisco Louzada Neto
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs
• Processo FAPESP: 18/14819-5 - Aprendizado de máquina automático: aprendendo a aprender
• Beneficiário: Edesio Pinto de Souza Alcobaça Neto
• Modalidade de apoio: Bolsas no Brasil - Doutorado Direto
• Processo FAPESP: 16/18615-0 - Aprendizado de máquina avançado
• Beneficiário: André Carlos Ponce de Leon Ferreira de Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Parceria para Inovação Tecnológica - PITE