UnderBagging based reduced Kernelized weighted extreme learning machine for class imbalance learning

Raghuwanshi, Bhagat Singh; Shukla, Sanyam

doi:10.1016/j.engappai.2018.07.002

Cited by 43 publications

(17 citation statements)

References 35 publications

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“…Adaptive kNN [69] inversed k-INOS [70] neural networks balanced GRSOM [71]; Radial-based oversampling [72]; SMOTE-CSELM [73]; UBRKWELM [74]; UBKELM [75] ensemble balanced RUSBagging [55,76];…”

Section: Class Distribution-based Methodsmentioning

confidence: 99%

“…BalancedEnsemble [79]; GRSOM [71]; UBRKWELM [74]; UBKELM [75] varied by settings SMOTEBoost [80] inversed Inverse-imbalance Bagging [81] class overlap-based clustering not balanced OBU [45]; BoostOBU [3]; DBMUTE [56] not balanced MWMOTE [60] balanced A-SUWO [14]; NI-MWMOTE [82] neighbourhood search balanced ADASYN [83]; LR-SMOTE [84] not balanced NCL [85]; PNN [86]; NB-based undersampling [33] noise removal not balanced SMOTE-IPF [87];…”

Section: Class Distribution-based Methodsmentioning

confidence: 99%

“…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%

“…ELM is a single-layer feed-forward neural network that uses a random approach to generate the hidden layer weights. This enables its training speed to be faster than other gradient-based algorithms [74].…”

Section: Class Distribution-based Methodsmentioning

confidence: 99%

“…The authors exploited this benefit of ELM, and since the traditional ELM was not designed for imbalanced data, they proposed to use many techniques to rebalance the data such as class-specific regularization parameters computed based on the class distribution [115], SMOTE [73] and UnderBagging [74,75].…”

Section: Class Distribution-based Methodsmentioning

confidence: 99%

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On the class overlap problem in imbalanced data classification

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Section: Class Distribution-based Methodsmentioning

confidence: 99%

Section: Class Distribution-based Methodsmentioning

confidence: 99%

Section: Class Distribution-based Methodsmentioning

confidence: 99%

Section: Class Distribution-based Methodsmentioning

confidence: 99%

Section: Class Distribution-based Methodsmentioning

confidence: 99%

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On the class overlap problem in imbalanced data classification

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Computing Model for Real-Time Online Fraudulent Identification

Ramniklal,

Zalavadia

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Algorithms for Intelligent Systems

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Class-specific extreme learning machine based on overall distribution for addressing binary imbalance problem

Raghuwanshi¹

2022

Soft Comput

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Imbalanced classification problems have significantly unequal class proportions. Conventional extreme learning machine (ELM) gives the same importance to all the samples leading to the results which favor the majority class. To solve this intrinsic deficiency, modification of ELM have been developed like weighted ELM (WELM), WELM based on the overall distribution (ODW-ELM) etc.. We have also recently developed class-specific ELM (CS-ELM) in one of our recent works. It has been shown in this work that the derivation of the output weights, β is more efficient compared to class-specific cost regulation ELM (CCRELM) for handling the class imbalance problem. Motivated by CCR-ELM X. Luo et al. has proposed the classifier ODW-ELM which is also not efficient for the imbalance learning. In this work, a novel class-specific WELM based on overall distribution (OD-CSELM) and the kernelized version of OD-CSELM (OD-CSKELM) is proposed to address the binary class imbalance problem more effectively. OD-CSELM and OD-CSKELM are motivated by CS-ELM. In addition, the computational complexities of OD-CSELM and OD-CSKELM are significantly lower than WELM and kernelized WELM respectively. The proposed work is evaluated by using the benchmark real-world imbalanced datasets. The experimental results demonstrate that the proposed work gives good generalization performance in contrast with rest of the classifiers for class imbalance learning.

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UnderBagging based reduced Kernelized weighted extreme learning machine for class imbalance learning

Cited by 43 publications

References 35 publications

On the class overlap problem in imbalanced data classification

On the class overlap problem in imbalanced data classification

Computing Model for Real-Time Online Fraudulent Identification

Class-specific extreme learning machine based on overall distribution for addressing binary imbalance problem

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