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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Projections as visual aids for classification system design

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Autor(es):
Rauber, Paulo E. [1, 2] ; Falcao, Alexandre X. [1] ; Telea, Alexandru C. [2]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Estadual Campinas, Campinas, SP - Brazil
[2] Univ Groningen, Dept Math & Comp Sci, Nijenborgh 9, NL-9747 AG Groningen - Netherlands
Número total de Afiliações: 2
Tipo de documento: Artigo Científico
Fonte: INFORMATION VISUALIZATION; v. 17, n. 4, p. 282-305, OCT 2018.
Citações Web of Science: 5
Resumo

Dimensionality reduction is a compelling alternative for high-dimensional data visualization. This method provides insight into high-dimensional feature spaces by mapping relationships between observations (high-dimensional vectors) to low (two or three) dimensional spaces. These low-dimensional representations support tasks such as outlier and group detection based on direct visualization. Supervised learning, a subfield of machine learning, is also concerned with observations. A key task in supervised learning consists of assigning class labels to observations based on generalization from previous experience. Effective development of such classification systems depends on many choices, including features descriptors, learning algorithms, and hyperparameters. These choices are not trivial, and there is no simple recipe to improve classification systems that perform poorly. In this context, we first propose the use of visual representations based on dimensionality reduction (projections) for predictive feedback on classification efficacy. Second, we propose a projection-based visual analytics methodology, and supportive tooling, that can be used to improve classification systems through feature selection. We evaluate our proposal through experiments involving four datasets and three representative learning algorithms. (AU)

Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
Beneficiário:Alexandre Xavier Falcão
Linha de fomento: Auxílio à Pesquisa - Temático
Processo FAPESP: 12/24121-9 - Anotação multi-rótulos de bancos de imagens naturais com supervisão mínima
Beneficiário:Paulo Eduardo Rauber
Linha de fomento: Bolsas no Brasil - Doutorado Direto