| Full text | |
| Author(s): |
Total Authors: 3
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| Affiliation: | [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
Total Affiliations: 3
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| Document type: | Journal article |
| Source: | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE; v. 26, n. 2 MAR 2012. |
| Web of Science Citations: | 6 |
| Abstract | |
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) | |
| FAPESP's process: | 10/05647-4 - Digital forensics: collection, organization, classification and analysis of digital evidences |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Grants - Young Investigators Grants |