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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.)

Low false positive learning with support vector machines

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Autor(es):
Moraes, Daniel [1] ; Wainer, Jacques [1] ; Rocha, Anderson [1]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Univ Estadual Campinas, Inst Comp, Av Albert Einstein 1251, BR-13083852 Campinas, SP - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION; v. 38, p. 340-350, JUL 2016.
Citações Web of Science: 2
Resumo

Most machine learning systems for binary classification are trained using algorithms that maximize the accuracy and assume that false positives and false negatives are equally bad. However, in many applications, these two types of errors may have very different costs. In this paper, we consider the problem of controlling the false positive rate on SVMs, since its traditional formulation does not'offer such assurance. To solve this problem, we define a feature space sensitive area, where the probability of having false positives is higher, and use a second classifier (unanimity k-NN) in this area to better filter errors and improve the decision-making process. We call this method Risk Area SVM (RA-SVM). We compare the RA-SVM to other state-of-the-art methods for low false positive classification using 33 standard datasets in the literature. The solution we propose shows better performance in the vast majority of the cases using the standard Neyman-Pearson measure. (C) 2016 Elsevier Inc. All rights reserved. (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