Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/320183
Type: Artigo
Title: Low false positive learning with support vector machines
Author: Moraes, Daniel
Wainer, Jacques
Rocha, Anderson
Abstract: 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.
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 dif
Subject: Máquina de vetores de suporte
Aprendizado de máquina
Algoritmos de computador
Country: Reino Unido
Editor: Elsevier
Citation: Journal Of Visual Communication And Image Representation. ACADEMIC PRESS INC ELSEVIER SCIENCE, n. 38, p. 340 - 350.
Rights: fechado
Fechado
Identifier DOI: 10.1016/j.jvcir.2016.03.007
Address: https://www.sciencedirect.com/science/article/pii/S1047320316300116
Date Issue: 2016
Appears in Collections:IC - Artigos e Outros Documentos

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