Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images

S., Akinbo, Racheal; Daramola, Oladunni A.

doi:10.5772/intechopen.100602

Cited by 12 publications

(6 citation statements)

References 21 publications

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“…Stacking can aid in the reduction of overfitting, which happens when a model performs well on the training data but fails to generalize to new, untried data. Stacked ensembles can lessen overfitting and enhance generalization performance by mixing many models with distinct biases, which is the idea behind implementing the SEDL model (Akinbo and Daramola, 2021 ). Another issue that frequently arises in machine learning models is the bias-variance trade-off, partly addressed by stacked ensemble learning, the SEDL model.…”

Section: Resultsmentioning

confidence: 99%

Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting

Bakasa,

Viriri

2023

Front. Artif. Intell.

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Ensemble learning aims to improve prediction performance by combining several models or forecasts. However, how much and which ensemble learning techniques are useful in deep learning-based pipelines for pancreas computed tomography (CT) image classification is a challenge. Ensemble approaches are the most advanced solution to many machine learning problems. These techniques entail training multiple models and combining their predictions to improve the predictive performance of a single model. This article introduces the idea of Stacked Ensemble Deep Learning (SEDL), a pipeline for classifying pancreas CT medical images. The weak learners are Inception V3, VGG16, and ResNet34, and we employed a stacking ensemble. By combining the first-level predictions, an input train set for XGBoost, the ensemble model at the second level of prediction, is created. Extreme Gradient Boosting (XGBoost), employed as a strong learner, will make the final classification. Our findings showed that SEDL performed better, with a 98.8% ensemble accuracy, after some adjustments to the hyperparameters. The Cancer Imaging Archive (TCIA) public access dataset consists of 80 pancreas CT scans with a resolution of 512 * 512 pixels, from 53 male and 27 female subjects. A sample of two hundred and twenty-two images was used for training and testing data. We concluded that implementing the SEDL technique is an effective way to strengthen the robustness and increase the performance of the pipeline for classifying pancreas CT medical images. Interestingly, grouping like-minded or talented learners does not make a difference.

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Section: Resultsmentioning

confidence: 99%

Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting

Bakasa,

Viriri

2023

Front. Artif. Intell.

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“…Ensemble models are categorized into three types: bagging, stacking, and boosting. Bagging is concerned with making many decisions on different samples of the same dataset and calculating the average prediction; stacking is concerned with fitting various models on the same data while using another model to learn the combined predictions [15]. Similarly, boosting entails adding ensemble members successively to correct prior predictions made by other models, yielding the average of the predictions.…”

Section: Background and Related Workmentioning

confidence: 99%

An Optimized Bagging Ensemble Learning Approach Using BESTrees for Predicting Students’ Performance

Evangelista

2023

Int. J. Emerg. Technol. Learn.

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Every academic institution's goal is to identify students who require additional assistance and take appropriate actions to improve their performance. As such, various research studies have focused on developing prediction models that can detect correlated patterns influencing students' performance, dropout, collaboration, and engagement. Among the influential predictive models available, the bagging ensemble has captured the interest of researchers seeking to improve prediction accuracy over single classifiers. However, prior work in this area has focused mainly on selecting single classifiers as the base classifier of the bagging ensemble, with little to no further optimization of the proposed framework. This study aims to fill this gap by providing a bagging ensemble framework to optimize its hyperparameters and achieve improved prediction accuracy. The proposed model used the Weka BESTrees data mining tool and Math language course student dataset from UCI Machine Learning Repository. Based on the experiments performed, the proposed bagging optimization technique can effectively increase the accuracy of a traditional bagging ensemble method. It reveals further that the proposed BESTrees framework can achieve an optimized performance when trained with the appropriate hyperparameters and hill climb metrics.

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“…used ensemble-based methods (bagged regression trees, random forests, and boosted regression trees) to directly estimate average treatment effects by imputing potential outcomes to compare inferences on the effect of in-hospital smoking cessation counseling on subsequent mortality in patients hospitalized with acute myocardial infarction. Ensemble methods are successfully applied in various research areas like medical image processing(Akinbo and Daramola (2021)), and severe weather forecasting(Khalaf et al …”

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confidence: 99%

A new intuitionistic fuzzy time series method based on the bagging of decision trees and principal component analysis

Yücesoy,

Egrioglu,

Bas

2023

Granul. Comput.

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Intuitionistic fuzzy time series methods provide a good alternative to the forecasting problem. It is possible to use the historical values of the time series as well as the membership and non-membership values obtained for the historical values as effective factors in improving the forecasting performance. In this study, a high order single variable intuitionistic fuzzy time series reduced forecasting model is first introduced. A new forecasting method is proposed for the solution of the forecasting problem in which the functional structure between the historical information of the intuitionistic time series and the forecast is obtained by bagging of decision trees based on the high order single variable intuitionistic fuzzy time series reduced forecasting model.In the proposed method, the intuitionistic fuzzy c-means clustering method is used to create intuitionistic fuzzy time series. To create a simpler functional structure with Bagging of decision trees, the input data from lagged variables, memberships, and non-membership values are subjected to dimension reduction by principal component analysis. The performance of the proposed method is compared with popular forecasting methods in the literature for ten different time series randomly obtained from the S&P500 stock market. According to the results of the analyses, the forecasting performance of the proposed method is better than both classical forecasting methods and some popular shallow and deep neural networks. KeywordsForecasting . Intuitionistic fuzzy time series . Bagging of decision tree . Principal component analysis . Intuitionistic fuzzy c-means clustering

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Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images

Cited by 12 publications

References 21 publications

Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting

Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting

An Optimized Bagging Ensemble Learning Approach Using BESTrees for Predicting Students’ Performance

A new intuitionistic fuzzy time series method based on the bagging of decision trees and principal component analysis

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