Artículos de revistas
HOW FAR DO WE GET USING MACHINE LEARNING BLACK-BOXES?
Registro en:
International Journal Of Pattern Recognition And Artificial Intelligence. World Scientific Publ Co Pte Ltd, v. 26, n. 2, 2012.
0218-0014
WOS:000308104300007
10.1142/S0218001412610010
Autor
Rocha, A
Papa, JP
Meira, LAA
Institución
Resumen
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) 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. 26 2 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) University of Campinas PAPDIC Grant [519.292-340/10] Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Microsoft Research Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) FAPESP [2009/16206-1, 2010/05647-4] University of Campinas PAPDIC Grant [519.292-340/10]