Class-specific kernelized extreme learning machine for binary class imbalance learning

Raghuwanshi, Bhagat Singh; Shukla, Sanyam

doi:10.1016/j.asoc.2018.10.011

Cited by 44 publications

(21 citation statements)

References 28 publications

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“…Several solutions based on neural networks have also been recently proposed [71,72,73,75]. In [71] [73,75,115,74,116]. ELM is a single-layer feed-forward neural network that uses a random approach to generate the hidden layer weights.…”

Section: Class Distribution-based Methodsmentioning

confidence: 99%

On the class overlap problem in imbalanced data classification

Vuttipittayamongkol

Elyan

Petrovski

2021

Knowledge-Based Systems

160

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Section: Class Distribution-based Methodsmentioning

confidence: 99%

On the class overlap problem in imbalanced data classification

Vuttipittayamongkol

Elyan

Petrovski

2021

Knowledge-Based Systems

160

View full text Add to dashboard Cite

“…Sigrist, F. and Hirnschall, C. (2019) (Sigrist and Hirnschall 2019) introduced a new Grabit model of binary classification to solve the class imbalance. Raghuwanshi, B. and Shukla, S. (2018) (Raghuwanshi and Shukla 2018) used the variant class nucleation of ELM to effectively address class imbalance. Ma, X. et.…”

Section: Research On Processing Methods Of Imbalanced Datamentioning

confidence: 99%

“…represents the proportion of TP samples in the total number of predicted positive samples. = + is the "true case rate" representing the ratio of true positive instances to all positive instances (Raghuwanshi and Shukla 2018).…”

Section: Evaluation Metricsmentioning

confidence: 99%

CUS-LightGBM-based financial distress prediction for small- and medium-sized enterprises with imbalanced data

Ruan

Zhang

2021

Preprint

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In this paper, by analyzing the financial data of small- and medium-sized enterprises, it is found that there is a general problem of imbalance data. Therefore, an effective financial distress prediction model based on clustering under-sampling and LightGBM model is constructed. Based on the idea of ensemble learning, this paper proposes the CUS-LightGBM (cluster-based under-sampling with LightGBM) model. First, the data are divided into minority and majority class samples. Then the K-means algorithm is used to cluster the majority class samples, and some data are selected from each cluster to form the balanced data. Finally, it is fused with the LightGBM algorithm based on decision tree to form an efficient prediction model. In addition, there are a large number of redundant features in the proposed model, which will reduce the prediction accuracy and efficiency of the model. Therefore, this paper adopts the feature selection based on ensemble strategy, and determines the main risk factors according to the principle of minority obeying majority. Finally, through the experimental analysis of real financial data, the results show that the CUS-LightGBM model can significantly improve the recognition ability of small- and medium-sized enterprises in financial distress, and the proposed model is more effective in processing financial ratio data than the benchmark model.

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“…A softmax activation function is used in the output layer where the layer size is the same as the number of data classes. In the case of binary class, a sigmoid activation function is applied as per convention with only one hidden unit [41]. The bearing model architecture with the number of parameters is presented in Table 4.…”

Section: Deep Learning Classifiermentioning

confidence: 99%

Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis

Faysal

Ngui

Lim

et al. 2021

Sensors

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Rotating machinery is one of the major components of industries that suffer from various faults due to the constant workload. Therefore, a fast and reliable fault diagnosis method is essential for machine condition monitoring. In this study, noise eliminated ensemble empirical mode decomposition (NEEEMD) was used for fault feature extraction. A convolution neural network (CNN) classifier was applied for classification because of its feature learning ability. A generalized CNN architecture was proposed to reduce the model training time. A sample size of 64×64×3 pixels RGB scalograms are used as the classifier input. However, CNN requires a large number of training data to achieve high accuracy and robustness. Deep convolution generative adversarial network (DCGAN) was applied for data augmentation during the training phase. To evaluate the effectiveness of the proposed feature extraction method, scalograms from related feature extraction methods such as ensemble empirical mode decomposition (EEMD), complementary EEMD (CEEMD), and continuous wavelet transform (CWT) are classified. The effectiveness of scalograms is also validated by comparing the classifier performance using grayscale samples from the raw vibration signals. All the outputs from bearing and blade fault classifiers showed that scalogram samples from the proposed NEEEMD method obtained the highest accuracy, sensitivity, and robustness using CNN. DCGAN was applied with the proposed NEEEMD scalograms to further increase the CNN classifier’s performance and identify the optimal number of training data. After training the classifier using augmented samples, the results showed that the classifier obtained even higher validation and test accuracy with greater robustness. The proposed method can be used as a more generalized and robust method for rotating machinery fault diagnosis.

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Class-specific kernelized extreme learning machine for binary class imbalance learning

Cited by 44 publications

References 28 publications

On the class overlap problem in imbalanced data classification

On the class overlap problem in imbalanced data classification

CUS-LightGBM-based financial distress prediction for small- and medium-sized enterprises with imbalanced data

Noise Eliminated Ensemble Empirical Mode Decomposition Scalogram Analysis for Rotating Machinery Fault Diagnosis

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