Twin Neural Networks for the classification of large unbalanced datasets

Jayadeva,; Pant, Harsh V.; Sharma, Mayank; Soman, Sumit

doi:10.1016/j.neucom.2018.07.089

Cited by 23 publications

(4 citation statements)

References 54 publications

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“…For example, Jayadeva et al. [54] , [55] introduced a non-iterative method for adding samples to small datasets. This technique estimates the data in a sub-space of its eigenvectors, clusters them in the sub-space, and produces additional samples within the clusters.…”

Section: Proposed Methodologymentioning

confidence: 99%

A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images

Naeem

Bin-Salem

2021

Applied Soft Computing

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Auto-detection of diseases has become a prime issue in medical sciences as population density is fast growing. An intelligent framework for disease detection helps physicians identify illnesses, give reliable and consistent results, and reduce death rates. Coronavirus (Covid-19) has recently been one of the most severe and acute diseases in the world. An automatic detection framework should therefore be introduced as the fastest diagnostic alternative to avoid Covid-19 spread. In this paper, an automatic Covid-19 identification in the CT scan and chest X-ray is obtained with the help of a combined deep learning and multi-level feature extraction methodology. In this method, the multi-level feature extraction approach comprises GIST, Scale Invariant Feature Transform (SIFT), and Convolutional Neural Network (CNN) extract features from CT scans and chest X-rays. The objective of multi-level feature extraction is to reduce the training complexity of CNN network, which significantly assists in accurate and robust Covid-19 identification. Finally, Long Short-Term Memory (LSTM) along the CNN network is used to detect the extracted Covid-19 features. The Kaggle SARS-CoV-2 CT scan dataset and the Italian SIRM Covid-19 CT scan and chest X-ray dataset were employed for testing purposes. Experimental outcomes show that proposed approach obtained 98.94% accuracy with the SARS-CoV-2 CT scan dataset and 83.03% accuracy with the SIRM Covid-19 CT scan and chest X-ray dataset. The proposed approach helps radiologists and practitioners to detect and treat Covid-19 cases effectively over the pandemic.

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Section: Proposed Methodologymentioning

confidence: 99%

A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images

Naeem

Bin-Salem

2021

Applied Soft Computing

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“…Experiments on several stateof-the-art ensemble models are performed to verify the effectiveness of the EGHE model. ey are an EMPNGAbased multistage hybrid model put forward by Zhang and Xia [37]; the heterogeneous ensemble credit model put forward by Xia et al [40]; EBCA-RF&XGB-PSO model that is put forward by He et al [41]; heterogeneous ensemble learning-based two-stage credit risk model (TSHE) proposed by Papouskova and Hajek [42]; twin neural networks (TNN) proposed by Jayadeva et al [43]; and a new rule-based knowledge extraction (RKE) method proposed by Mahani and Baba [44] recently. Table 8 gives the results of ensemble models in different data sets.…”

Section: Experimental Results Different Methods Are Utilized As Comparison Models To Test the Validity Of The Eghe Credit Scoring Modelmentioning

confidence: 99%

A Novel Ensemble Credit Scoring Model Based on Extreme Learning Machine and Generalized Fuzzy Soft Sets

Zhang

et al. 2020

Mathematical Problems in Engineering

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This paper mainly discusses the hybrid application of ensemble learning, classification, and feature selection (FS) algorithms simultaneously based on training data balancing for helping the proposed credit scoring model perform more effectively, which comprises three major stages. Firstly, it conducts preprocessing for collected credit data. Then, an efficient feature selection algorithm based on adaptive elastic net is employed to reduce the weakly related or uncorrelated variables to get high-quality training data. Thirdly, a novel ensemble strategy is proposed to make the imbalanced training data set balanced for each extreme learning machine (ELM) classifier. Finally, a new weighting method for single ELM classifiers in the ensemble model is established with respect to their classification accuracy based on generalized fuzzy soft sets (GFSS) theory. A novel cosine-based distance measurement algorithm of GFSS is also proposed to calculate the weights of each ELM classifier. To confirm the efficiency of the proposed ensemble credit scoring model, we implemented experiments with real-world credit data sets for comparison. The process of analysis, outcomes, and mathematical tests proved that the proposed model is capable of improving the effectiveness of classification in average accuracy, area under the curve (AUC), H-measure, and Brier’s score compared to all other single classifiers and ensemble approaches.

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“…1. To further ensure credibility of our findings and handle associated inconsistencies resulting from randomization and stochastic nature of the training process, the experiment for each combination was repeated five times with varying random seeds; resulting in different initial parameters for each repetition [47]. The mean validation results across the repetitions are reported.…”

Section: Methodsmentioning

confidence: 99%

Investigating Group Distributionally Robust Optimization for Deep Imbalanced Learning: A Case Study of Binary Tabular Data Classification

Mustapha¹,

Hasan²,

Nabbus³

et al. 2023

IJACSA

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One of the most studied machine learning challenges that recent studies have shown the susceptibility of deep neural networks to is the class imbalance problem. While concerted research efforts in this direction have been notable in recent years, findings have shown that the canonical learning objective, empirical risk minimization (ERM), is unable to achieve optimal imbalance learning in deep neural networks given its bias to the majority class. An alternative learning objective, group distributionally robust optimization (gDRO), is investigated in this study for imbalance learning, focusing on tabular imbalanced data as against image data that has dominated deep imbalance learning research. Contrary to minimizing average per instance loss as in ERM, gDRO seeks to minimize the worst group loss over the training data. Experimental findings in comparison with ERM and classical imbalance methods using four popularly used evaluation metrics in imbalance learning across several benchmark imbalance binary tabular data of varying imbalance ratios reveal impressive performance of gDRO, outperforming other compared methods in terms of g-mean and roc-auc.

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Twin Neural Networks for the classification of large unbalanced datasets

Cited by 23 publications

References 54 publications

A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images

A CNN-LSTM network with multi-level feature extraction-based approach for automated detection of coronavirus from CT scan and X-ray images

A Novel Ensemble Credit Scoring Model Based on Extreme Learning Machine and Generalized Fuzzy Soft Sets

Investigating Group Distributionally Robust Optimization for Deep Imbalanced Learning: A Case Study of Binary Tabular Data Classification

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