An Auto-Adjustable Semi-Supervised Self-Training Algorithm

Livieris, Ioannis E.; Kanavos, Andreas; Tampakas, Vassilis; Pintelas, Panayiotis E.

doi:10.3390/a11090139

Cited by 22 publications

(18 citation statements)

References 48 publications

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“…Furthermore, the base learners utilized in all self-labeled algorithms are the Sequential Minimum Optimization (SMO) [33], the C4.5 decision tree algorithm [34] and the kNN algorithm [35] as in [2,[7][8][9], which probably constitute the most effective and popular machine learning algorithms for classification problems [36].…”

Section: Performance Evaluation Of Wvensl Against Ensemble Self-labelmentioning

confidence: 99%

“…The statistical comparison of several classification algorithms over multiple datasets is fundamental in the area of machine learning and it is usually performed by means of a statistical test [2,[7][8][9]. Since our motivation stems from the fact that we are interested in evaluating the rejection of the hypothesis that all the algorithms perform equally well for a given level based on their classification accuracy and highlighting the existence of significant differences between our proposed algorithm and the classical self-labeled algorithms, we utilized the non-parametric Friedman Aligned Ranking (FAR) [37] test.…”

Section: Performance Evaluation Of Wvensl Against Ensemble Self-labelmentioning

confidence: 99%

“…These algorithms efficiently develop powerful classifiers by meaningfully relating the explicit classification information of labeled data with the information hidden in the unlabeled data [3,4]. Self-labeled algorithms probably constitute the most popular class of SSL algorithms due to their simplicity of implementation, their wrapper-based philosophy and good classification performance [2,[5][6][7][8]. This class of algorithms exploits a large amount of unlabeled data via a self-learning process based on supervised learners.…”

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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: Performance Evaluation Of Wvensl Against Ensemble Self-labelmentioning

confidence: 99%

Section: Performance Evaluation Of Wvensl Against Ensemble Self-labelmentioning

confidence: 99%

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confidence: 99%

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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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“…In machine learning, the statistical comparison of several evaluation algorithms over multiple datasets is fundamental and it is frequently performed by means of a statistical test [20,21,56]. Since our motivation stems from the fact that we are interested in evaluating the rejection of the hypothesis that all the algorithms perform equally well for a given level based on their classification accuracy and highlighting the existence of significant differences between our proposed algorithm and the classical self-labeled algorithms, we used the non-parametric Friedman Aligned Ranking (FAR) [57] test.…”

Section: First Phase Of Experimentsmentioning

confidence: 99%

On Ensemble SSL Algorithms for Credit Scoring Problem

et al. 2018

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Credit scoring is generally recognized as one of the most significant operational research techniques used in banking and finance, aiming to identify whether a credit consumer belongs to either a legitimate or a suspicious customer group. With the vigorous development of the Internet and the widespread adoption of electronic records, banks and financial institutions have accumulated large repositories of labeled and mostly unlabeled data. Semi-supervised learning constitutes an appropriate machine- learning methodology for extracting useful knowledge from both labeled and unlabeled data. In this work, we evaluate the performance of two ensemble semi-supervised learning algorithms for the credit scoring problem. Our numerical experiments indicate that the proposed algorithms outperform their component semi-supervised learning algorithms, illustrating that reliable and robust prediction models could be developed by the adaptation of ensemble techniques in the semi-supervised learning framework.

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“…Averaging (simple or weighted [13]) and voting (majority, unanimity, plurality, or even weighted votes) are popular and commonly used combination methods [14] depending on the problem which needs to be resolved [10]. Moreover, approaches using committees of base learners into the core of their learning process have also been demonstrated recently, presenting encouraging results [15].…”

Section: Introductionmentioning

confidence: 99%

A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems

2020

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In recent years, a forward-looking subfield of machine learning has emerged with important applications in a variety of scientific fields. Semi-supervised learning is increasingly being recognized as a burgeoning area embracing a plethora of efficient methods and algorithms seeking to exploit a small pool of labeled examples together with a large pool of unlabeled ones in the most efficient way. Co-training is a representative semi-supervised classification algorithm originally based on the assumption that each example can be described by two distinct feature sets, usually referred to as views. Since such an assumption can hardly be met in real world problems, several variants of the co-training algorithm have been proposed dealing with the absence or existence of a naturally two-view feature split. In this context, a Static Selection Ensemble-based co-training scheme operating under a random feature split strategy is outlined regarding binary classification problems, where the type of the base ensemble learner is a soft-Voting one composed of two participants. Ensemble methods are commonly used to boost the predictive performance of learning models by using a set of different classifiers, while the Static Ensemble Selection approach seeks to find the most suitable structure of ensemble classifier based on a specific criterion through a pool of candidate classifiers. The efficacy of the proposed scheme is verified through several experiments on a plethora of benchmark datasets as statistically confirmed by the Friedman Aligned Ranks non-parametric test over the behavior of classification accuracy, F1-score, and Area Under Curve metrics.

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An Auto-Adjustable Semi-Supervised Self-Training Algorithm

Cited by 22 publications

References 48 publications

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

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

On Ensemble SSL Algorithms for Credit Scoring Problem

A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems

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