###### Abstract  
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. machine learning; semi-supervised learning; self-labeled algorithms; classifiers; ensemble learning; weighted voting; image classification; lung abnormalities +
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of machine learning and data mining techniques for the development of intelligent computational systems in order to extract useful and valuable information. As a result, the areas of biomedical research and diagnostic medicine have been dramatically transformed, from rather qualitative sciences which were based on observations of whole organisms to more quantitative sciences which are now based on the extraction of useful knowledge from a large amount of data [2]. Nevertheless, distinguishing the various chest abnormalities from CXRs is a rather challenging task, not only for a prediction model but even for an human expert. The progress in the field has been hampered by the lack of available labeled images for efficiently training a powerful and accurate supervised classification model. Moreover, the process of correctly labeling new unlabeled CXRs usually incurs monetary costs and high time since it constitutes a long and complicated process and requires the efforts of specialized personnel and expert physicians. Semi-Supervised Learning (SSL) algorithms have been proposed as a new direction to address the problem of shortage of available labeled data, comprising characteristics of both supervised and unsupervised learning algorithms. 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. In other words, they perform an iterative procedure, enriching the initial labeled data, based on the assumption that their own predictions tend to be correct. Recently, [PERSON] et al. [9] proposed an in-depth taxonomy based on the main characteristics presented in them and conducted a comprehensive research of their classification efficacy on several datasets. Generally, self-labeled algorithm can be classified in two main groups: _Self-training_ and _Co-training_. In the original Self-training [10], a single classifier is iteratively trained on an enlarged labeled dataset with its most confident predictions on unlabeled data while in Co-training [11], two classifiers are separately trained utilizing two different views on a labeled dataset and then each classifier augments the labeled data of the other with its most confident predictions on unlabeled data.