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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.)

HOW FAR DO WE GET USING MACHINE LEARNING BLACK-BOXES?

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Autor(es):
Rocha, Anderson [1] ; Papa, Joao Paulo [2] ; Meira, Luis A. A. [3]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Campinas UNICAMP, Inst Comp, BR-13083852 Campinas, SP - Brazil
[2] UNESP Univ Estadual Paulista, Dept Comp Sci, BR-17033360 Bauru, SP - Brazil
[3] Univ Campinas UNICAMP, Fac Technol, BR-13484332 Limeira, SP - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE; v. 26, n. 2 MAR 2012.
Citações Web of Science: 6
Resumo

With several good research groups actively working in machine learning (ML) approaches, we have now the concept of self-containing machine learning solutions that oftentimes work out-of-the-box leading to the concept of ML black-boxes. Although it is important to have such black-boxes helping researchers to deal with several problems nowadays, it comes with an inherent problem increasingly more evident: we have observed that researchers and students are progressively relying on ML black-boxes and, usually, achieving results without knowing the machinery of the classifiers. In this regard, this paper discusses the use of machine learning black-boxes and poses the question of how far we can get using these out-of-the-box solutions instead of going deeper into the machinery of the classifiers. The paper focuses on three aspects of classifiers: (1) the way they compare examples in the feature space; (2) the impact of using features with variable dimensionality; and (3) the impact of using binary classifiers to solve a multi-class problem. We show how knowledge about the classifier's machinery can improve the results way beyond out-of-the-box machine learning solutions. (AU)

Processo FAPESP: 10/05647-4 - Computação forense e criminalística de documentos: coleta, organização, classificação e análise de evidências
Beneficiário:Anderson de Rezende Rocha
Linha de fomento: Auxílio à Pesquisa - Apoio a Jovens Pesquisadores