Gaussian Sampling Approach to deal with Imbalanced Telemetry Datasets in Industrial Applications

被引：0

作者：

Galve, Sergio ^{[1
]}

Puig, Vicenc ^{[2
]}

Vilajosana, Xavi ^{[1
]}

机构：

[1] Univ Oberta Catalunya, Wireless Networks Res Lab, Castelldefels 08860, Barcelona, Spain

[2] UPC, CSIC, Inst Robot & Informat Ind, Barcelona, Spain

来源：

2023 31ST MEDITERRANEAN CONFERENCE ON CONTROL AND AUTOMATION, MED | 2023年

关键词：

D O I：

10.1109/MED59994.2023.10185829

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Practical implementation of data analytics in industrial environments has always been a problematic area because of data availability and quality. In this paper, a Gaussian sampling methodology is proposed to address the problem of imbalanced telemetry datasets that is one of the root causes that make modelling less reliable. By generating subsets that achieve homogeneous density distributions this problem is addressed. By comparing the impact of this method with the baseline case of random sampling, this paper aims to address this problem and propose a practical solution. A case study based on an industrial cooling device is used to assess and illustrate the proposed approach.

引用

页码：605 / 611

页数：7

共 50 条

[31] Experimental Comparison of Sampling Techniques for Imbalanced Datasets Using Various Classification Models
Pattanayak, Sanjibani Sudha
Rout, Minakhi
PROGRESS IN ADVANCED COMPUTING AND INTELLIGENT ENGINEERING, VOL 2, 2018, 564 : 13 - 22
[32] An Evolutionary Sampling Approach for Classification with Imbalanced Data
Fernandes, Everlandio R. Q.
de Carvalho, Andre C. P. L. F.
Coelho, Andre L. V.
2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2015,
[33] A Hybrid Sampling Method Based on Safe Screening for Imbalanced Datasets with Sparse Structure
Shi, Hongbo
Gao, Qigang
Ji, Suqin
Liu, Yanxin
2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,
[34] Machine Learning with Imbalanced EEG Datasets using Outlier-based Sampling
Islah, Nizar
Koerner, Jamie
Genov, Roman
Valiante, Taufik A.
O'Leary, Gerard
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20, 2020, : 112 - 115
[35] A Novel Evolutionary Preprocessing Method Based on Over-sampling and Under-sampling for Imbalanced Datasets
Wong, Ginny Y.
Leung, Frank H. F.
Ling, Sai-Ho
39TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY (IECON 2013), 2013, : 2354 - 2359
[36] A dual algorithmic approach to deal with multiclass imbalanced classification problems
Sridhar, S.
Anusuya, S.
BIG DATA RESEARCH, 2024, 38
[37] AWGAN: An adaptive weighting GAN approach for oversampling imbalanced datasets
Guan, Shaopeng
Zhao, Xiaoyan
Xue, Yuewei
Pan, Hao
INFORMATION SCIENCES, 2024, 663
[38] An efficient classification approach in imbalanced datasets for intrinsic plagiarism detection
Andrianna Polydouri
Eleni Vathi
Georgios Siolas
Andreas Stafylopatis
Evolving Systems, 2020, 11 : 503 - 515
[39] An efficient classification approach in imbalanced datasets for intrinsic plagiarism detection
Polydouri, Andrianna
Vathi, Eleni
Siolas, Georgios
Stafylopatis, Andreas
EVOLVING SYSTEMS, 2020, 11 (03) : 503 - 515
[40] Data-Centric Optimization Approach for Small, Imbalanced Datasets
Tanov, Vladislav
JOURNAL OF INFORMATION AND ORGANIZATIONAL SCIENCES, 2023, 47 (01) : 167 - 177

← 1 2 3 4 5 →