A Brief Review on Instance Selection Based on Condensed Nearest Neighbors for Data Classification Tasks


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Output type: Journal article

UM6P affiliated Publication?: No

Author list: Yasmany Fernández-Fernández, Diego H. Peluffo-Ordóñez, Ana C. Umaquinga-Criollo, Leandro L. Lorente-Leyva, Elia N. Cabrera-Alvarez

Publication year: 2021

Journal: Lecture Notes in Electrical Engineering (1876-1100)

ISSN: 1876-1100

URL: https://link.springer.com/chapter/10.1007/978-***********-4_23


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Abstract

The condensed nearest neighbor (CNN) classifier is one of the techniques used and known to perform recognition tasks. It has also proven to be one of the most interesting algorithms in the field of data mining despite its simplicity. However, CNN suffers from several drawbacks, such as high storage requirements and low noise tolerance. One of the characteristics of CNN is that it focuses on the selection of prototypes, which consists of reducing the set of training data. One of the goals of CNN seeks to achieve the reduction of information in such a way that the reduced information can represent large amounts of data to exercise decision-making on them. This paper mentions some of the most recent contributions to CNN-based unsupervised algorithms in a review that builds on the mathematical principles of condensed methods.


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Last updated on 2021-14-10 at 23:16