Autor(es)

Pimentel, Bruno Almeida ; de Carvalho, Andre C. P. L. E. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2019-01-01.

Resumo

In this work, the goal is to use clustering algorithms as recommender in a meta-learning system and, thus, to propose an unsupervised meta-learning approach. Meta-learning has been successfully used for recommendation of Machine Learning algorithms in several Data Mining tasks. Meta-learning can rank algorithms according to their adequacy for a new dataset and use this ranking to recommend algorithms. The recommendations are usually made by predictive meta-models induced by supervised Machine Learning techniques, therefore needing a target attribute. In many situations, the target attribute is not available or has a high computational cost. In these situations, the use of unsupervised meta-models (as clustering algorithms) could provide important insights from Machine Learning experiments, like the interpretation of the partitions found by these clustering algorithms. Here, clustering algorithms are used as unsupervised meta-models. Experimental results show that the proposed approach achieved better clustering quality. (AU)

• Processo FAPESP: 12/22608-8 - Uso de medidas de complexidade de dados no suporte ao aprendizado de máquina supervisionado
• Beneficiário: Ana Carolina Lorena
• Modalidade de apoio: Auxílio à Pesquisa - Jovens Pesquisadores
• Processo FAPESP: 17/20265-0 - Uso de meta-aprendizado para seleção de algoritmos em problemas de agrupamento
• Beneficiário: Bruno Almeida Pimentel
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• 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