{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T19:55:14Z","timestamp":1770753314555,"version":"3.50.0"},"reference-count":25,"publisher":"World Scientific Pub Co Pte Lt","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2012,3]]},"abstract":"<jats:p> 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. <\/jats:p>","DOI":"10.1142\/s0218001412610010","type":"journal-article","created":{"date-parts":[[2012,3,16]],"date-time":"2012-03-16T00:24:58Z","timestamp":1331857498000},"page":"1261001","source":"Crossref","is-referenced-by-count":26,"title":["HOW FAR DO WE GET USING MACHINE LEARNING BLACK-BOXES?"],"prefix":"10.1142","volume":"26","author":[{"given":"ANDERSON","family":"ROCHA","sequence":"first","affiliation":[{"name":"Institute of Computing, University of Campinas (UNICAMP), Avenida Albert Einstein, 1251 \u2014 Cidade Universit\u00e1ria, 13083-852, Campinas, SP, Brazil"}]},{"given":"JO\u00c3O PAULO","family":"PAPA","sequence":"additional","affiliation":[{"name":"Department of Computer Science, UNESP \u2014 University Estadual Paulista, Avenida Engenheiro Luiz Edmundo Carrijo Coube, 14-01, 17033-360, Bauru, SP, Brazil"}]},{"given":"LUIS A. A.","family":"MEIRA","sequence":"additional","affiliation":[{"name":"Faculty of Technology, University of Campinas (UNICAMP), Rua Paschoal Marmo, 1888 \u2014 Jardim Nova It\u00e1lia, 13484-332, Limeira, SP, Brazil"}]}],"member":"219","published-online":{"date-parts":[[2012,8,28]]},"reference":[{"key":"rf1","first-page":"113","volume":"1","author":"Allwein E.","journal-title":"J. Mach. Learn. Res."},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1109\/72.363444"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2009.06.012"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2006.242"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(03)00002-3"},{"key":"rf6","volume-title":"Clustering and Information Retrieval","author":"Baeza-Yates R.","year":"2003"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-28349-8_2"},{"key":"rf9","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop C. 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