Analyzing meta-data from public repositories

• Grant number: 24/07637-9
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: June 01, 2024
• End date: August 31, 2025
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: Ana Carolina Lorena
• Grantee: Lucas Ribeiro do Rêgo Barros
• Host Institution: Divisão de Ciência da Computação (IEC). Instituto Tecnológico de Aeronáutica (ITA). Ministério da Defesa (Brasil). São José dos Campos , SP, Brazil
• Associated research grant: 21/06870-3 - Beyond algorithm selection: meta-learning for data and algorithm analysis and understanding, AP.JP2
• Associated scholarship(s): 24/16535-5 - Integrating an algorithm recommendation module into the PyISpace package, BE.EP.IC

Abstract

The OpenML repository is widely employed by the Machine Learning (ML) community, storing a large number of datasets along with the results of com-putational experiments run on the same data. Some meta-analysis of ML datasets are also provided in the MATILDA repository (Melbourne Algorithm Test Instance Library with Data Analytics). The objective of this work is to analyze meta-datasetsgenerated from such repositories joining the characteristics of the OpenML datasets and also the performance achieved by different ML techniques in their solution, in order to support meta-learning tasks.