Ensemble learning-based filter-centric hybrid feature selection framework for high-dimensional imbalanced data

Kim, Jongmo; Kang, Jaewoong; Sohn, Mye M.

doi:10.1016/j.knosys.2021.106901

Cited by 28 publications

(10 citation statements)

References 28 publications

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“…Feature selection is commonly considered as a necessary preprocessing step for classification tasks in the context of class imbalance [20][21][22][23] to construct an optimal feature subset. The commonly used feature selection methods can be mainly categorized into three types: Filter, Wrapper and Embedded [24].…”

Section: Introductionmentioning

confidence: 99%

Attention features selection oversampling technique (AFS-O) for rolling bearing fault diagnosis with class imbalance

Han,

Wang,

Shen

et al. 2023

Meas. Sci. Technol.

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When using data-driven methods for fault diagnosis of mechanical rotating components such as gears and bearings, there is a problem of class imbalance in the lifecycle data collected by sensors. The most commonly used method to address this issue is the synthetic minority over-sampling technique, which synthesizes samples in the feature space, but its blind synthesis may lead to redundant features in the synthetic samples. To avoid this deficiency, this paper proposes a feature-weighted oversampling method called AFS-O (Attention Features Selection Oversampling Technique). First, time–frequency domain features are extracted from the full lifecycle data of bearings to construct an initial subset of features, which serves the input to AFS. Then, AFS is then used to obtain the distribution of feature selection patterns and generate feature weights to determine the inclusion or exclusion of each feature, thereby constructing an optimal subset of features. Finally, the optimal feature subset is synthetically oversampled to achieve class-balanced data, which is then fed into a classifier. AFS-O is applied to the rolling bearing accelerated lifetime dataset from Xi’an Jiaotong University. Experimental results demonstrate that AFS-O outperforms other state-of-the-art synthetic oversampling algorithms in terms of Gmean, F 2 score , and Recall, confirming the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Attention features selection oversampling technique (AFS-O) for rolling bearing fault diagnosis with class imbalance

Han,

Wang,

Shen

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…As for dimension reduction, lots of methods have been proposed so far. However, some are applied to domain-specific [ 5 , 6 , 7 , 8 ], some are aimed at solving high-dimension problems [ 29 ], some are to solve the problem of unbalanced samples, some are focused on the relationship between the features [ 13 , 30 ], and some are adopted heuristic algorithms. They are limited to a particular field or condition.…”

Section: Introductionmentioning

confidence: 99%

“…Feature selection aims to select the most applicable features while ignoring the irrelevant and redundant ones, and feature selection methods are generally divided into three types: filter, wrapper and embedded [ 6 , 10 , 11 , 20 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors

Wang

2022

Sensors

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For permanent magnet DC motors (PMDCMs), the amplitude of the current signals gradually decreases after the motor starts. In this work, the time domain features and time-frequency-domain features extracted from several successive segments of current signals make up a feature vector, which is adopted for fault diagnosis of PMDCMs. Many redundant features will lead to a decrease in diagnosis efficiency and increase the computation cost, so it is necessary to eliminate redundant features and features that have negative effects. This paper presents a novel supervised filter feature selection method for reducing data dimension by employing the Gaussian probability density function (GPDF) and named Gaussian vote feature selection (GVFS). To evaluate the effectiveness of the proposed GVFS, we compared it with the other five filter feature selection methods by utilizing the PMDCM’s data. Additionally, Gaussian naive Bayes (GNB), k-nearest neighbor algorithm (k-NN), and support vector machine (SVM) are utilized for the construction of fault diagnosis models. Experimental results show that the proposed GVFS has a better diagnostic effect than the other five feature selection methods, and the average accuracy of fault diagnosis improves from 97.89% to 99.44%. This paper lays the foundation of fault diagnosis for PMDCMs and provides a novel filter feature selection method.

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“…Many literature studies show that feature selection methods based on metaheuristics have excellent performance in solving common feature selection problems. However, with the expansion of search space, especially when the number of features reaches thousands, its calculation cost will increase exponentially [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hybrid Feature Selection Method Using the Artificial Immune Algorithm for High-Dimensional Data

Zhu

2022

Computational Intelligence and Neuroscience

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Feature selection provides the optimal subset of features for data mining models. However, current feature selection methods for high-dimensional data also require a better balance between feature subset quality and computational cost. In this paper, an efficient hybrid feature selection method (HFIA) based on artificial immune algorithm optimization is proposed to solve the feature selection problem of high-dimensional data. The algorithm combines filter algorithms and improves clone selection algorithms to explore the feature space of high-dimensional data. According to the target requirements of feature selection, combined with biological research results, this method introduces the lethal mutation mechanism and the Cauchy operator to improve the search performance of the algorithm. Moreover, the adaptive adjustment factor is introduced in the mutation and update phases of the algorithm. The effective combination of these mechanisms enables the algorithm to obtain a better search ability and lower computational costs. Experimental comparisons with 19 state-of-the-art feature selection methods are conducted on 25 high-dimensional benchmark datasets. The results show that the feature reduction rate for all datasets is above 99%, and the performance improvement for the classifier is between 5% and 48.33%. Compared with the five classical filtering feature selection methods, the computational cost of HFIA is lower than the two of them, and it is far better than these five algorithms in terms of the feature reduction rate and classification accuracy improvement. Compared with the 14 hybrid feature selection methods reported in the latest literature, the average winning rates in terms of classification accuracy, feature reduction rate, and computational cost are 85.83%, 88.33%, and 96.67%, respectively.

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Ensemble learning-based filter-centric hybrid feature selection framework for high-dimensional imbalanced data

Cited by 28 publications

References 28 publications

Attention features selection oversampling technique (AFS-O) for rolling bearing fault diagnosis with class imbalance

Attention features selection oversampling technique (AFS-O) for rolling bearing fault diagnosis with class imbalance

A Novel Supervised Filter Feature Selection Method Based on Gaussian Probability Density for Fault Diagnosis of Permanent Magnet DC Motors

An Efficient Hybrid Feature Selection Method Using the Artificial Immune Algorithm for High-Dimensional Data

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