Busca avançada
Ano de início
Entree


Unsupervised Meta-Learning for Clustering Algorithm Recommendation

Texto completo
Autor(es):
Pimentel, Bruno Almeida ; de Carvalho, Andre C. P. L. E. ; IEEE
Número total de Autores: 3
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