Imbalanced Data Fault Diagnosis Based on an Evolutionary Online Sequential Extreme Learning Machine

Wu, Hao; Liu, Feng

doi:10.3390/sym12081204

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…In the classification domain, most of the real-world problems are class imbalanced. Examples of such problems are cancer detection [1,2], fault detection [3], intrusion detection system [4], software test optimization [5], speech quality assessment [6], pressure prediction [7], etc. In a problem when the number of samples in one class outnumbers the numbers of samples in some other class, it is considered as a class imbalanced/asymmetric problem.…”

Section: Introductionmentioning

confidence: 99%

Reduced-Kernel Weighted Extreme Learning Machine Using Universum Data in Feature Space (RKWELM-UFS) to Handle Binary Class Imbalanced Dataset Classification

Choudhary

Shukla

2022

Symmetry

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Class imbalance is a phenomenon of asymmetry that degrades the performance of traditional classification algorithms such as the Support Vector Machine (SVM) and Extreme Learning Machine (ELM). Various modifications of SVM and ELM have been proposed to handle the class imbalance problem, which focus on different aspects to resolve the class imbalance. The Universum Support Vector Machine (USVM) incorporates the prior information in the classification model by adding Universum data to the training data to handle the class imbalance problem. Various other modifications of SVM have been proposed which use Universum data in the classification model generation. Moreover, the existing ELM-based classification models intended to handle class imbalance do not consider the prior information about the data distribution for training. An ELM-based classification model creates two symmetry planes, one for each class. The Universum-based ELM classification model tries to create a third plane between the two symmetric planes using Universum data. This paper proposes a novel hybrid framework called Reduced-Kernel Weighted Extreme Learning Machine Using Universum Data in Feature Space (RKWELM-UFS) to handle the classification of binary class-imbalanced problems. The proposed RKWELM-UFS combines the Universum learning method with a Reduced-Kernelized Weighted Extreme Learning Machine (RKWELM) for the first time to inherit the advantages of both techniques. To generate efficient Universum samples in the feature space, this work uses the kernel trick. The performance of the proposed method is evaluated using 44 benchmark binary class-imbalanced datasets. The proposed method is compared with 10 state-of-the-art classifiers using AUC and G-mean. The statistical t-test and Wilcoxon signed-rank test are used to quantify the performance enhancement of the proposed RKWELM-UFS compared to other evaluated classifiers.

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Section: Introductionmentioning

confidence: 99%

Reduced-Kernel Weighted Extreme Learning Machine Using Universum Data in Feature Space (RKWELM-UFS) to Handle Binary Class Imbalanced Dataset Classification

Choudhary

Shukla

2022

Symmetry

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“…FL-NIDS [16] overcomes the imbalanced data problem and is applied to evaluate three benchmark intrusion detection datasets that suffer from imbalanced distributions. OS-ELM [17] uses the oversampling technique when identifying an axle box bearing fault of multiple highspeed electric units.…”

Section: Introductionmentioning

confidence: 99%

A New Oversampling Method Based on the Classification Contribution Degree

et al. 2021

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Data imbalance is a thorny issue in machine learning. SMOTE is a famous oversampling method of imbalanced learning. However, it has some disadvantages such as sample overlapping, noise interference, and blindness of neighbor selection. In order to address these problems, we present a new oversampling method, OS-CCD, based on a new concept, the classification contribution degree. The classification contribution degree determines the number of synthetic samples generated by SMOTE for each positive sample. OS-CCD follows the spatial distribution characteristics of original samples on the class boundary, as well as avoids oversampling from noisy points. Experiments on twelve benchmark datasets demonstrate that OS-CCD outperforms six classical oversampling methods in terms of accuracy, F1-score, AUC, and ROC.

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“…However, in many practical applications, a few samples are more valuable than most other samples. (1,2) Thus, classification of imbalanced data is an important research topic in machine learning and data mining as the accuracy of algorithms depends on how correctly the data are classified. Data mining for imbalanced data can be performed using a decision tree (DT), artificial neural network (ANN), genetic algorithm (GA), and support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Algorithm Based on Simulated Annealing and Bacterial Foraging Optimization for Mining Imbalanced Data

Lee¹,

Lee²,

Huang³

et al. 2021

Sensors and Materials

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The bacterial foraging optimization (BFO) algorithm can simulate the mechanism of natural selection. However, as the direction of inversion is uncertain in the chemotaxis process, it easily falls into a local optimum. We propose a hybrid algorithm based on simulated annealing (SA) and BFO for mining imbalanced data. The key idea is to exploit the advantages of both SA and the BFO algorithm. In the proposed algorithm, SA finds the optimal solution by employing a jump process, so as to solve the uncertainty of the reversal direction in the chemotaxis process of BFO and avoid falling into a local optimum. SA is used to improve the chemotaxis process of BFO, and then the swarming process, reproduction process, and eliminationdispersal process of BFO are implemented. Four imbalanced datasets are used to test the performance of the proposed hybrid algorithm. In each imbalanced dataset used for testing, there is a certain correlation between the variables, making the dataset multivariate. Through the proposed algorithm, these four multivariate imbalanced datasets are effectively classified, and its performance compared with that of other algorithms. Experimental results show that for the different multivariate imbalanced datasets, the proposed algorithm is better than the original BFO algorithm in terms of various performance indicators. By combining the proposed algorithm with sensor-related technology, in the future, medical multivariate data and security monitoring system data obtained by sensors can be analyzed to improve the classification accuracy of multivariate data.

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Imbalanced Data Fault Diagnosis Based on an Evolutionary Online Sequential Extreme Learning Machine

Cited by 18 publications

References 15 publications

Reduced-Kernel Weighted Extreme Learning Machine Using Universum Data in Feature Space (RKWELM-UFS) to Handle Binary Class Imbalanced Dataset Classification

Reduced-Kernel Weighted Extreme Learning Machine Using Universum Data in Feature Space (RKWELM-UFS) to Handle Binary Class Imbalanced Dataset Classification

A New Oversampling Method Based on the Classification Contribution Degree

Hybrid Algorithm Based on Simulated Annealing and Bacterial Foraging Optimization for Mining Imbalanced Data

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