Journal
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE
Volume 10, Issue 3, Pages 52-60Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MCI.2015.2437512
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Funding
- National Science Foundation (NSF) [ECCS 1053717, CCF 1439011]
- Army Research Office [W911NF-12-1-0378]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1439011] Funding Source: National Science Foundation
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This article introduces a new supervised classification method - the extended nearest neighbor (ENN) - that predicts input patterns according to the maximum gain of intra-class coherence. Unlike the classic k-nearest neighbor (KNN) method, in which only the nearest neighbors of a test sample are used to estimate a group membership, the ENN method makes a prediction in a two-way communication style: it considers not only who are the nearest neighbors of the test sample, but also who consider the test sample as their nearest neighbors. By exploiting the generalized class-wise statistics from all training data by iteratively assuming all the possible class memberships of a test sample, the ENN is able to learn from the global distribution, therefore improving pattern recognition performance and providing a powerful technique for a wide range of data analysis applications.
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