Imbalanced data classification using MapReduce and relief

Imbalanced data classification using MapReduce and relief

Classification of imbalanced data has been reported to require modification of standard classification algorithms and lately has attracted a lot of attention due to practical applications in industry, banking and finance. The aim of the paper is to examine algorithms known from literature when two m...

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Bibliographic Details
Main Authors: Joanna Jedrzejowicz, Robert Kostrzewski, Jakub Neumann, Magdalena Zakrzewska
Format: Article
Language:English
Published: Taylor & Francis Group 2018-04-01
Series:Journal of Information and Telecommunication
Subjects:
Imbalanced data
classification
parallelization
feature selection
Online Access:http://dx.doi.org/10.1080/24751839.2018.1440454
Description
Summary:Classification of imbalanced data has been reported to require modification of standard classification algorithms and lately has attracted a lot of attention due to practical applications in industry, banking and finance. The aim of the paper is to examine algorithms known from literature when two modifications are introduced: MapReduce to parallelize computations and Relief to select most valuable attributes. Both modifications are needed in Big Data area. Also two new algorithms are considered.
ISSN:2475-1839
2475-1847

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