DTCo: An Ensemble SSL Algorithm for X-ray Classification

Livieris, Ioannis E.; Kotsilieris, Theodore; Anagnostopoulos, Ioannis; Tampakas, Vassilis

doi:10.1007/978-3-030-32622-7_24

Cited by 2 publications

(3 citation statements)

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“…Additionally, their experiments lead them to the conclusion that reliable and robust prediction models could be developed utilizing a few labeled and many unlabeled data. In [16] the authors extended the previous work and proposed DTCo algorithm for the classification of X-rays. The proposed ensemble algorithm exploits the predictions of Democratic-Co learning, Tri-training and Co-Bagging utilizing a maximum-probability voting scheme.…”

Section: Related Workmentioning

confidence: 99%

“…• "WvEnSL 1 (kNN)" stands for Algorithm WvEnSL using the same components classifiers as CST-Voting (kNN). • "DTCo" stands for an ensemble of Democratic-Co learning, Tri-training and Co-Bagging with C4.5 as base learner using majority voting [16]. • "WvEnSL 2 " stands for Algorithm WvEnSL using the same components classifiers as DTCo.…”

Section: Performance Evaluation Of Wvensl Against Ensemble Self-labelmentioning

confidence: 99%

“…Each bootstrap sample contains about 2/3 of the original training set, where each example can appear multiple times. Recently, a new approach has been given by Livieris et al [2,8,16,17] and Livieris [18] in which some ensemble self-labeled algorithms are proposed based on voting schemes. The proposed algorithms exploit the individual predictions of the most efficient and frequently used self-labeled algorithms using simple voting methodologies.Motivated by these works, we propose a new semi-supervised self-labeled algorithm which is based on a sophisticated ensemble philosophy.…”

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A Weighted Voting Ensemble Self-Labeled Algorithm for the Detection of Lung Abnormalities from X-Rays

et al. 2019

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During the last decades, intensive efforts have been devoted to the extraction of useful knowledge from large volumes of medical data employing advanced machine learning and data mining techniques. Advances in digital chest radiography have enabled research and medical centers to accumulate large repositories of classified (labeled) images and mostly of unclassified (unlabeled) images from human experts. Machine learning methods such as semi-supervised learning algorithms have been proposed as a new direction to address the problem of shortage of available labeled data, by exploiting the explicit classification information of labeled data with the information hidden in the unlabeled data. In the present work, we propose a new ensemble semi-supervised learning algorithm for the classification of lung abnormalities from chest X-rays based on a new weighted voting scheme. The proposed algorithm assigns a vector of weights on each component classifier of the ensemble based on its accuracy on each class. Our numerical experiments illustrate the efficiency of the proposed ensemble methodology against other state-of-the-art classification methods.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluation Of Wvensl Against Ensemble Self-labelmentioning

confidence: 99%

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