Nearest neighbors distance ratio open-set classifier

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• 作者：Pedro R. Mendes Júnior ; Roberto M. de Souza ; Rafael de O. Werneck…
• 关键词：Open ; set recognition ; Nearest neighbor classifier ; Open ; set nearest ; neighbor classifier ; Nearest neighbors distance ratio ; Open ; set evaluation measures
• 刊名：Machine Learning
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：106
• 期：3
• 页码：359-386
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Computing Methodologies; Simulation and Modeling; Language Translation and Linguistics;
• 出版者：Springer US
• ISSN：1573-0565
• 卷排序：106

文摘

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.