A-Stacking and A-Bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection

Agarwal, Shivang; Chowdary, C. Ravindranath

doi:10.1016/j.eswa.2019.113160

Cited by 88 publications

(34 citation statements)

References 40 publications

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“…Individual classifier may not be able to learn more information, while ensemble learning can improve the performance of a single classifier by combining them [ 28 ]. Ensemble learning is one of the most useful strategies to improve generalization performance of prediction model, with a core of training strategy for base classifiers, such as bagging, boosting, and stacking [ 29 ]. Bagging and boosting build the base learners from a single dataset, having an impact on diversity, while stacking learning method uses the multiple classifiers by taking the prediction of the previous level as input variables for the next level [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

Jia

et al. 2020

International Journal of Biomedical Imaging

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In the study of pediatric automatic bone age assessment (BAA) in clinical practice, the extraction of the object area in hand radiographs is an important part, which directly affects the prediction accuracy of the BAA. But no perfect segmentation solution has been found yet. This work is to develop an automatic hand radiograph segmentation method with high precision and efficiency. We considered the hand segmentation task as a classification problem. The optimal segmentation threshold for each image was regarded as the prediction target. We utilized the normalized histogram, mean value, and variance of each image as input features to train the classification model, based on ensemble learning with multiple classifiers. 600 left-hand radiographs with the bone age ranging from 1 to 18 years old were included in the dataset. Compared with traditional segmentation methods and the state-of-the-art U-Net network, the proposed method performed better with a higher precision and less computational load, achieving an average PSNR of 52.43 dB, SSIM of 0.97, DSC of 0.97, and JSI of 0.91, which is more suitable in clinical application. Furthermore, the experimental results also verified that hand radiograph segmentation could bring an average improvement for BAA performance of at least 13%.

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Section: Methodsmentioning

confidence: 99%

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

Jia

et al. 2020

International Journal of Biomedical Imaging

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“…Stacking technology is a general integration algorithm that integrates advanced learners by using multiple lower-level learners to achieve higher performance (Agarwal and Chowdary, 2020). In general, the K-fold cross-validation method is used to train and test these models and then output the prediction results.…”

Section: Stacking Modelmentioning

confidence: 99%

Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

et al. 2021

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Solar radiation is the Earth’s primary source of energy and has an important role in the surface radiation balance, hydrological cycles, vegetation photosynthesis, and weather and climate extremes. The accurate prediction of solar radiation is therefore very important in both the solar industry and climate research. We constructed 12 machine learning models to predict and compare daily and monthly values of solar radiation and a stacking model using the best of these algorithms were developed to predict solar radiation. The results show that meteorological factors (such as sunshine duration, land surface temperature, and visibility) are crucial in the machine learning models. Trend analysis between extreme land surface temperatures and the amount of solar radiation showed the importance of solar radiation in compound extreme climate events. The gradient boosting regression tree (GBRT), extreme gradient lifting (XGBoost), Gaussian process regression (GPR), and random forest models performed better (poor) prediction capabilities of daily and monthly solar radiation. The stacking model, which included the GBRT, XGBoost, GPR, and random forest models, performed better than the single models in the prediction of daily solar radiation but showed no advantage over the XGBoost model in the prediction of the monthly solar radiation. We conclude that the stacking model and the XGBoost model are the best models to predict solar radiation.

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“…In [9], A-Stacking and A-Bagging, the adaptive versions of ensemble learning approaches are proposed. A-Bagging method has been applied by using the same base learners over numerous subsets of data and the predictions are aggregated by using weighted majority voting.…”

Section: Related Workmentioning

confidence: 99%

A Stacking-based Ensemble Learning Method for Outlier Detection

Abro

2021

Preprint

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Outlier detection is considered as one of the crucial research areas for data mining. Many methods have been studied widely and utilized for achieving better results in outlier detection from existing literature; however, the effects of these few ways are inadequate. In this paper, a stacking-based ensemble classifier has been proposed along with four base learners (namely, Rotation Forest, Random Forest, Bagging and Boosting) and a Meta-learner (namely, Logistic Regression) to progress the outlier detection performance. The proposedmechanism is evaluated on five datasets from the ODDS library by adopting five performance criteria. The experimental outcomes demonstrate that the proposed method outperforms than the conventional ensemble approaches concerning the accuracy, AUC (Area Under Curve), precision, recall and Fmeasure values.This method can be used for image recognition and machine learning problems, such as binary classification.

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A-Stacking and A-Bagging: Adaptive versions of ensemble learning algorithms for spoof fingerprint detection

Cited by 88 publications

References 40 publications

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

Ensemble Learning with Multiclassifiers on Pediatric Hand Radiograph Segmentation for Bone Age Assessment

Solar Radiation Prediction Using Different Machine Learning Algorithms and Implications for Extreme Climate Events

A Stacking-based Ensemble Learning Method for Outlier Detection

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