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
收藏
页码：217 / 230
页数：14
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