Imbalanced data classification using MapReduce and relief

被引：6

作者：

Jedrzejowicz, Joanna ^{[1
]}

Kostrzewski, Robert ^{[1
]}

Neumann, Jakub ^{[1
]}

Zakrzewska, Magdalena ^{[1
]}

机构：

[1] Univ Gdansk, Fac Math Phys & Informat, Inst Informat, PL-80308 Gdansk, Poland

来源：

JOURNAL OF INFORMATION AND TELECOMMUNICATION | 2018年 / 2卷 / 02期

关键词：

Imbalanced data; classification; parallelization; feature selection;

D O I：

10.1080/24751839.2018.1440454

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：217 / 230

页数：14

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