Undersampling Instance Selection for Hybrid and Incomplete Imbalanced Data

被引：0

作者：

Camacho-Nieto, Oscar ^{[1
]}

Yanez-Marquez, Cornelio ^{[2
]}

Villuendas-Rey, Yenny ^{[1
]}

机构：

[1] Inst Politecn Nacl, CIDETEC, Cdmx, Mexico

[2] Inst Politecn Nacl, CIC, Cdmx, Mexico

来源：

JOURNAL OF UNIVERSAL COMPUTER SCIENCE | 2020年 / 26卷 / 06期

关键词：

undersampling; imbalanced data; hybrid and incomplete data; SOFTWARE TOOL; DATA-SETS; CLASSIFICATION; ALGORITHMS; ENSEMBLES; KEEL;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

This paper proposes a novel undersampling method, for dealing with imbalanced datasets. The proposal is based on a novel instance importance measure (also introduced in this paper), and is able to balance hybrid and incomplete data. The numerical experiments carried out show the proposed undersampling algorithm outperforms others algorithms of the state of art, in well-known imbalanced datasets.

引用

页码：698 / 719

页数：22

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