Artículos de revistas
HOW FAR do WE GET USING MACHINE LEARNING BLACK-BOXES?
Fecha
2012-03-01Registro en:
International Journal of Pattern Recognition and Artificial Intelligence. Singapore: World Scientific Publ Co Pte Ltd, v. 26, n. 2, p. 23, 2012.
0218-0014
10.1142/S0218001412610010
WOS:000308104300007
9039182932747194
Autor
Universidade Estadual de Campinas (UNICAMP)
Universidade Estadual Paulista (Unesp)
Institución
Resumen
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.