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Nearest neighbors distance ratio open-set classifier

Mendes Júnior, Pedro R. ; de Souza, Roberto M. ; Werneck, Rafael de O. ; Stein, Bernardo V. ; Pazinato, Daniel V. ; de Almeida, Waldir R. ; Penatti, Otávio A. B. ; Torres, Ricardo da S. ; Rocha, Anderson

Machine learning, 2017-03, Vol.106 (3), p.359-386 [Periódico revisado por pares]

New York: Springer US

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Título:
Nearest neighbors distance ratio open-set classifier
Autor: Mendes Júnior, Pedro R. ; de Souza, Roberto M. ; Werneck, Rafael de O. ; Stein, Bernardo V. ; Pazinato, Daniel V. ; de Almeida, Waldir R. ; Penatti, Otávio A. B. ; Torres, Ricardo da S. ; Rocha, Anderson
Assuntos: Artificial Intelligence ; Benchmarks ; Classification ; Classifiers ; Computer Science ; Control ; Machine learning ; Mechatronics ; Natural Language Processing (NLP) ; Parameters ; Recognition ; Resilience ; Robotics ; Simulation and Modeling ; Test procedures ; Training
É parte de: Machine learning, 2017-03, Vol.106 (3), p.359-386
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Descrição: In this paper, we propose a novel multiclass classifier for the open-set recognition scenario. This scenario is the one in which there are no a priori training samples for some classes that might appear during testing. Usually, many applications are inherently open set. Consequently, successful closed-set solutions in the literature are not always suitable for real-world recognition problems. The proposed open-set classifier extends upon the Nearest-Neighbor (NN) classifier. Nearest neighbors are simple, parameter independent, multiclass, and widely used for closed-set problems. The proposed Open-Set NN (OSNN) method incorporates the ability of recognizing samples belonging to classes that are unknown at training time, being suitable for open-set recognition. In addition, we explore evaluation measures for open-set problems, properly measuring the resilience of methods to unknown classes during testing. For validation, we consider large freely-available benchmarks with different open-set recognition regimes and demonstrate that the proposed OSNN significantly outperforms their counterparts in the literature.
Editor: New York: Springer US
Idioma: Inglês

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Nearest neighbors distance ratio open-set classifier

Mendes Júnior, Pedro R. ; de Souza, Roberto M. ; Werneck, Rafael de O. ; Stein, Bernardo V. ; Pazinato, Daniel V. ; de Almeida, Waldir R. ; Penatti, Otávio A. B. ; Torres, Ricardo da S. ; Rocha, Anderson

New York: Springer US

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