Rank-based classifiers for extremely high-dimensional gene expression data

Lausser, Ludwig; Schmid, Florian; Schirra, Lyn-Rouven; Wilhelm, Adalbert F. X.; Kestler, Hans A.

doi:10.1007/s11634-016-0277-3

Cited by 7 publications

(9 citation statements)

References 20 publications

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“…Strict protocols and specifications are needed to limit their influence a priori. Examples for existing data science methods to counteract noise effects are global normalization techniques [48], robustness procedures [8,42] or invariant models that are insensitive to data transformations [45]. Methods for aggregated data modalities Besides improving the analysis of single data modalities via additional samples and external domain knowledge, big data also brings up the challenge and promise of combining multiple data modalities (Fig.…”

Section: Data Sciencementioning

confidence: 99%

Big data and precision medicine: challenges and strategies with healthcare data

Kraus

Lausser

Kühn

et al. 2018

Int J Data Sci Anal

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Recent snapshots of the European progress on big data in health care and precision medicine reveal diverse perceptions of experts and the public, leading to the impression that algorithmic issues have the largest share among the challenges all health systems are faced with. Yet, from a comparison of different countries it is evident that the adaption and integration of heterogeneous data sources have a major impact on the advancement of precision medicine. Legal regulations for implementation and operation of healthcare networking are actively discussed in the public and gradually implemented in several countries. Based on a unified documentation, they are a perfect precondition for integrating distributed healthcare data to a big data platform with a reliable fact representation. Now, basic and clinical scientists have to be motivated to share their work with these data platforms. In this work, we aim to provide an overview on the common issues in big healthcare data applications and address the challenges for the involved scientific, clinical and administrative partners. We propose a possible strategy for a comprehensive data integration by iterating data harmonization, semantic enrichment and data analysis processes.

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Section: Data Sciencementioning

confidence: 99%

Big data and precision medicine: challenges and strategies with healthcare data

Kraus

Lausser

Kühn

et al. 2018

Int J Data Sci Anal

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“…The efficacy of combining a large number of individual classifiers, also called base learners, has been well studied [10]- [16]. The main advantage of combining the results of many variants of the same classifier is that it leads to a reduction in the generalization error of the resultant ensemble classifier [11]- [13], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Optimal Trees Selection for Classification via Out-of-Bag Assessment and Sub-Bagging

et al. 2021

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The effect of training data size on machine learning methods has been well investigated over the past two decades. The predictive performance of tree based machine learning methods, in general, improves with a decreasing rate as the size of training data increases. We investigate this in optimal trees ensemble (OTE) where the method fails to learn from some of the training observations due to internal validation. Modified tree selection methods are thus proposed for OTE to cater for the loss of training observations in internal validation. In the first method, corresponding out-of-bag (OOB) observations are used in both individual and collective performance assessment for each tree. Trees are ranked based on their individual performance on the OOB observations. A certain number of top ranked trees is selected and starting from the most accurate tree, subsequent trees are added one by one and their impact is recorded by using the OOB observations left out from the bootstrap sample taken for the tree being added. A tree is selected if it improves predictive accuracy of the ensemble. In the second approach, trees are grown on random subsets, taken without replacement-known as sub-bagging, of the training data instead of bootstrap samples (taken with replacement). The remaining observations from each sample are used in both individual and collective assessments for each corresponding tree similar to the first method. Analysis on 21 benchmark datasets and simulations studies show improved performance of the modified methods in comparison to OTE and other state-of-the-art methods.

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“…Various authors have suggested that combining weak models leads to efficient ensembles (Schapire 1990;Domingos 1996;Quinlan 1996;Maclin and Opitz 2011;Hothorn and Lausen 2003;Janitza et al 2015;Gul et al 2016b;Lausser et al 2016;Bolón-Canedo et al 2012;Bhardwaj et al 2016;Liberati et al 2017). Combining the outputs of multiple classifiers also reduces generalization error (Domingos 1996;Quinlan 1996;Bauer and Kohavi 1999;Maclin and Opitz 2011;Tzirakis and Tjortjis 2017).…”

Section: Introductionmentioning

confidence: 99%

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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Rank-based classifiers for extremely high-dimensional gene expression data

Cited by 7 publications

References 20 publications

Big data and precision medicine: challenges and strategies with healthcare data

Big data and precision medicine: challenges and strategies with healthcare data

Optimal Trees Selection for Classification via Out-of-Bag Assessment and Sub-Bagging

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

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