Improving binary classification using filtering based on k-NN proximity graphs

Improving binary classification using filtering based on k-NN proximity graphs

Abstract One of the ways of increasing recognition ability in classification problem is removing outlier entries as well as redundant and unnecessary features from training set. Filtering and feature selection can have large impact on classifier accuracy and area under the curve (AUC), as noisy data...

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Bibliographic Details
Main Authors: Maher Ala’raj, Munir Majdalawieh, Maysam F. Abbod
Format: Article
Language:English
Published: SpringerOpen 2020-03-01
Series:Journal of Big Data
Subjects:
Binary classification
Heterogeneous combiner
k-NN
Proximity graphs
Data filtering
Feature selection
Online Access:http://link.springer.com/article/10.1186/s40537-020-00297-7

Internet

http://link.springer.com/article/10.1186/s40537-020-00297-7

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