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Nearest neighbors distance ratio open-set classifier
Machine learning, 2017-03, Vol.106 (3), p.359-386
[Peer Reviewed Journal]
The Author(s) 2016 ;Machine Learning is a copyright of Springer, 2017. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-016-5610-8
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Title:
Nearest neighbors distance ratio open-set classifier
Author:
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
Subjects:
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
Is Part Of:
Machine learning, 2017-03, Vol.106 (3), p.359-386
Description:
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.
Publisher:
New York: Springer US
Language:
English
Identifier:
ISSN: 0885-6125
EISSN: 1573-0565
DOI: 10.1007/s10994-016-5610-8
Source:
AUTh Library subscriptions: ProQuest Central
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