Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 1 de 1
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
IEEE Trans Vis Comput Graph ; 28(9): 3307-3323, 2022 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-33439846

RESUMO

Visual analytics enables the coupling of machine learning models and humans in a tightly integrated workflow, addressing various analysis tasks. Each task poses distinct demands to analysts and decision-makers. In this survey, we focus on one canonical technique for rule-based classification, namely decision tree classifiers. We provide an overview of available visualizations for decision trees with a focus on how visualizations differ with respect to 16 tasks. Further, we investigate the types of visual designs employed, and the quality measures presented. We find that (i) interactive visual analytics systems for classifier development offer a variety of visual designs, (ii) utilization tasks are sparsely covered, (iii) beyond classifier development, node-link diagrams are omnipresent, (iv) even systems designed for machine learning experts rarely feature visual representations of quality measures other than accuracy. In conclusion, we see a potential for integrating algorithmic techniques, mathematical quality measures, and tailored interactive visualizations to enable human experts to utilize their knowledge more effectively.


Assuntos
Algoritmos , Gráficos por Computador , Árvores de Decisões , Humanos , Aprendizado de Máquina
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA
...