A review on classification of imbalanced data for wireless sensor networks

被引：103

作者：

Patel, Harshita ^{[1
]}

Rajput, Dharmendra Singh ^{[1
]}

Reddy, G. Thippa ^{[1
]}

Iwendi, Celestine ^{[2
]}

Bashir, Ali Kashif ^{[3
]}

Jo, Ohyun ^{[4
]}

机构：

[1] Vellore Inst Technol, Sch Informat Technol & Engn, Vellore 632014, Tamil Nadu, India

[2] BCC Cent South Univ Forestry & Technol, Dept Elect, Changsha, Peoples R China

[3] Manchester Metropolitan Univ, Dept Comp & Math, Manchester, Lancs, England

[4] Chungbuk Natl Univ, Dept Comp Sci, Cheongju, South Korea

来源：

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS | 2020年 / 16卷 / 04期

基金：

新加坡国家研究基金会;

关键词：

Wireless sensor networks; data mining; imbalanced data; data balancing; algorithm modification; ensemble techniques; K-NEAREST NEIGHBOR; SAMPLING APPROACH; NEURAL-NETWORKS; ALGORITHM; MACHINE; SMOTE; PERFORMANCE; EXTRACTION; SELECTION; STRATEGY;

D O I：

10.1177/1550147720916404

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 is a vastly explored issue of the last and present decade and still keeps the same importance because data are an essential term today and it becomes crucial when data are distributed into several classes. The term imbalance refers to uneven distribution of data into classes that severely affects the performance of traditional classifiers, that is, classifiers become biased toward the class having larger amount of data. The data generated from wireless sensor networks will have several imbalances. This review article is a decent analysis of imbalance issue for wireless sensor networks and other application domains, which will help the community to understand WHAT, WHY, and WHEN of imbalance in data and its remedies.

引用

页数：15

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