Ensemble Pruning for Glaucoma Detection in an Unbalanced Data Set

Adler, Werner; Gefeller, Olaf; Gul, Asma; Horn, Folkert K.; Khan, Zardad; Lausen, Berthold

doi:10.3414/me16-01-0055

Cited by 8 publications

(2 citation statements)

References 32 publications

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“…Regularized tree forest methods have been found effective in many studies 61 , 62 . Here we propose using the idea in 63 , called optimal trees ensemble (OTE) to prune the original forest for better results.…”

Section: Methodsmentioning

confidence: 99%

Regularized ensemble learning for prediction and risk factors assessment of students at risk in the post-COVID era

Khan,

Ali,

Khan

et al. 2024

Sci Rep

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The COVID-19 pandemic has had a significant impact on students’ academic performance. The effects of the pandemic have varied among students, but some general trends have emerged. One of the primary challenges for students during the pandemic has been the disruption of their study habits. Students getting used to online learning routines might find it even more challenging to perform well in face to face learning. Therefore, assessing various potential risk factors associated with students low performance and its prediction is important for early intervention. As students’ performance data encompass diverse behaviors, standard machine learning methods find it hard to get useful insights for beneficial practical decision making and early interventions. Therefore, this research explores regularized ensemble learning methods for effectively analyzing students’ performance data and reaching valid conclusions. To this end, three pruning strategies are implemented for the random forest method. These methods are based on out-of-bag sampling, sub-sampling and sub-bagging. The pruning strategies discard trees that are adversely affected by the unusual patterns in the students data forming forests of accurate and diverse trees. The methods are illustrated on an example data collected from university students currently studying on campus in a face-to-face modality, who studied during the COVID-19 pandemic through online learning. The suggested methods outperform all the other methods considered in this paper for predicting students at the risk of academic failure. Moreover, various factors such as class attendance, students interaction, internet connectivity, pre-requisite course(s) during the restrictions, etc., are identified as the most significant features.

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Section: Methodsmentioning

confidence: 99%

Regularized ensemble learning for prediction and risk factors assessment of students at risk in the post-COVID era

Khan,

Ali,

Khan

et al. 2024

Sci Rep

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“…Another motivation is that recent years have seen efforts to optimize ensemble methods. For example, the reduction of an ensemble such as a random forest by excluding trees with a low predictive power or redundant information can increase the overall prediction performance of the ensemble (Adler et al 2016;Kahn et al 2019Kahn et al , 2021.…”

Section: Introductionmentioning

confidence: 99%

Identification of representative trees in random forests based on a new tree-based distance measure

Laabs

Westenberger

König

2023

Adv Data Anal Classif

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In life sciences, random forests are often used to train predictive models. However, gaining any explanatory insight into the mechanics leading to a specific outcome is rather complex, which impedes the implementation of random forests into clinical practice. By simplifying a complex ensemble of decision trees to a single most representative tree, it is assumed to be possible to observe common tree structures, the importance of specific features and variable interactions. Thus, representative trees could also help to understand interactions between genetic variants. Intuitively, representative trees are those with the minimal distance to all other trees, which requires a proper definition of the distance between two trees. Thus, we developed a new tree-based distance measure, which incorporates more of the underlying tree structure than other metrics. We compared our new method with the existing metrics in an extensive simulation study and applied it to predict the age at onset based on a set of genetic risk factors in a clinical data set. In our simulation study we were able to show the advantages of our weighted splitting variable approach. Our real data application revealed that representative trees are not only able to replicate the results from a recent genome-wide association study, but also can give additional explanations of the genetic mechanisms. Finally, we implemented all compared distance measures in R and made them publicly available in the R package timbR (https://github.com/imbs-hl/timbR).

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Ensemble of optimal trees, random forest and random projection ensemble classification

Khan

Gul

Perperoglou

et al. 2019

Adv Data Anal Classif

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The predictive performance of a random forest ensemble is highly associated with the strength of individual trees and their diversity. Ensemble of a small number of accurate and diverse trees, if prediction accuracy is not compromised, will also reduce computational burden. We investigate the idea of integrating trees that are accurate and diverse. For this purpose, we utilize out-of-bag observations as a validation sample from the training bootstrap samples, to choose the best trees based on their individual performance and then assess these trees for diversity using the Brier score on an independent validation sample. Starting from the first best tree, a tree is selected for the final ensemble if its addition to the forest reduces error of the trees that have already been added. Our approach does not use an implicit dimension reduction for each tree as random project ensemble classification. A total of 35 bench mark problems on classification and regression are used to assess the performance of the proposed method and compare it with random forest, random projection ensemble, node harvest, support vector machine, kNN and classification and regression tree. We compute unexplained variances or classification error rates for all the methods on the corresponding data sets. Our experiments reveal that the size of the ensemble is reduced significantly and better results are obtained in most of the cases. Results of a simulation study are also given where four tree style scenarios are considered to generate data sets with several structures.

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Ensemble Pruning for Glaucoma Detection in an Unbalanced Data Set

Cited by 8 publications

References 32 publications

Regularized ensemble learning for prediction and risk factors assessment of students at risk in the post-COVID era

Regularized ensemble learning for prediction and risk factors assessment of students at risk in the post-COVID era

Identification of representative trees in random forests based on a new tree-based distance measure

Ensemble of optimal trees, random forest and random projection ensemble classification

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