Review of resampling techniques for the treatment of imbalanced industrial data classification in equipment condition monitoring

Yuan, Yage; Wei, Jianan; Huang, Haisong; Jiao, Weidong; Wang, Jiaxin; Chen, Hualin

doi:10.1016/j.engappai.2023.106911

Cited by 21 publications

(4 citation statements)

References 148 publications

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“…where Error represents the classification error rate, |S| represents the number of selected features, |F| represents the total number of features, and α is the weight assigned to the classification error rate, α ∈ [0, 1]. Table 3 shows eight binary transfer functions (including four S-shaped and four V-shaped transfer functions).…”

Section: Algorithmsmentioning

confidence: 99%

“…With the development of the information age, there is an explosive increase in data volume. The problems people encounter in fields such as engineering [1], ecology [2], information [3], manufacturing [4], design [5], and management [6] are becoming increasingly complex. Most of these problems exhibit characteristics such as multi-objective [7] and highdimensional [8].…”

Section: Introductionmentioning

confidence: 99%

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Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

Yao,

Yang,

Yuan

et al. 2023

Biomimetics

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The sand cat is a creature suitable for living in the desert. Sand cat swarm optimization (SCSO) is a biomimetic swarm intelligence algorithm, which inspired by the lifestyle of the sand cat. Although the SCSO has achieved good optimization results, it still has drawbacks, such as being prone to falling into local optima, low search efficiency, and limited optimization accuracy due to limitations in some innate biological conditions. To address the corresponding shortcomings, this paper proposes three improved strategies: a novel opposition-based learning strategy, a novel exploration mechanism, and a biological elimination update mechanism. Based on the original SCSO, a multi-strategy improved sand cat swarm optimization (MSCSO) is proposed. To verify the effectiveness of the proposed algorithm, the MSCSO algorithm is applied to two types of problems: global optimization and feature selection. The global optimization includes twenty non-fixed dimensional functions (Dim = 30, 100, and 500) and ten fixed dimensional functions, while feature selection comprises 24 datasets. By analyzing and comparing the mathematical and statistical results from multiple perspectives with several state-of-the-art (SOTA) algorithms, the results show that the proposed MSCSO algorithm has good optimization ability and can adapt to a wide range of optimization problems.

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Section: Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

Yao,

Yang,

Yuan

et al. 2023

Biomimetics

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show abstract

“…Many studies have explored how to overcome the problem of imbalanced data, but not many have actually applied it in the world of P2P Lending [13]. New methods have emerged to overcome the problem of imbalanced data [14]. There are two approaches that are usually used to overcome the problem of imbalanced data, namely the algorithmic level approach and the data level approach.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies that have been conducted can address the problem of balancing the amount of data [16]. The data resampling method is an approach that works from increasing the sample distribution by copying minority class samples or eliminating some samples in the majority class [14]. The advantage of using resampling techniques, including oversampling, is that the subsequent classification structure will remain the same and can be applied individually before entering the model training process [17].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Imbalanced Data Oversampling Techniques for Peer-to-Peer Landing Loan Prediction

Muzayanah,

Lestari,

Jumanto

et al. 2024

SJI

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Purpose: Data imbalances that often occur in the classification of loan data on the Peer-to-Peer Lending platform cancause algorithm performance to be less than optimal, causing the resulting accuracy to decrease. To overcome thisproblem, appropriate resampling techniques are needed so that the classification algorithm can work optimally andprovide results with optimal accuracy. This research aims to find the right resampling technique to overcome theproblem of data imbalance in data lending on peer-to-peer landing platforms.Methods: This study uses the XGBoost classification algorithm to evaluate and compare the resampling techniquesused. The resampling techniques that will be compared in this research include SMOTE, ADACYN, Border Line, andRandom Oversampling.Results: The highest training accuracy was achieved by the combination of the XGBoost model with the Boerder Lineresampling technique with a training accuracy of 0.99988 and the combination of the XGBoost model with the SMOTEresampling technique. In accuracy testing, the combination with the highest accuracy score was achieved by acombination of the XGBoost model with the SMOTE resampling technique.Novelty: It is hoped that from this research we can find the most suitable resampling technique combined with theXGBoost sorting algorithm to overcome the problem of unbalanced data in uploading data on peer-to-peer lendingplatforms so that the sorting algorithm can work optimally and produce optimal accuracy.

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Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications

Javed,

El-Sappagh,

Abuhmed

2024

Artif Intell Rev

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Review of resampling techniques for the treatment of imbalanced industrial data classification in equipment condition monitoring

Cited by 21 publications

References 148 publications

Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

Multi-Strategy Improved Sand Cat Swarm Optimization: Global Optimization and Feature Selection

Comparative Study of Imbalanced Data Oversampling Techniques for Peer-to-Peer Landing Loan Prediction

Robustness in deep learning models for medical diagnostics: security and adversarial challenges towards robust AI applications

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