Using Complexity Measures to Evolve Synthetic Classification Datasets

Autor(es)

de Melo, Vinicius V. ; Lorena, Ana C. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2018-01-01.

Resumo

Machine Learning studies usually involve a large volume of experimental work. For instance, any new technique or solution to a classification problem has to be evaluated concerning the predictive performance achieved in many datasets. In order to evaluate the robustness of the algorithm face to different class distributions, it would be interesting to choose a set of datasets that spans different levels of classification difficulty. In this paper, we present a method to generate synthetic classification datasets with varying complexity levels. The idea is to greedly exchange the labeling of a set of synthetically generated points in order to reach a given level of classification complexity, which is assessed by measures that estimate the difficulty of a classification problem based on the geometrical distribution of the data. (AU)

• Processo FAPESP: 17/20844-0 - Uma meta-heurística com auto-construção de operadores para otimização contínua global: extensões e aplicações do algoritmo de otimização por esquadrão de drones
• Beneficiário: Vinícius Veloso de Melo
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• 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