A Machine learning technique dedicated for biological data
Mohamed Nadjib Boufenara, Mahmoud Boufaïda, Mohamed Lamine Berkane · 2019
In a data-driven world, semi-supervised learning methods are motivated by the availability of large unlabeled datasets than a small amount of labeled data. However, incorporating unlabeled data into learning does not guarantee an improvement in classification performance. In this paper, we present an approach based on a deep learning system to predict missing classes by integrating a model of semi-supervised learning which is the self-training. In order to evaluate its performance, we used a set of diabetes data and four performance measures: Precision, Recall, F-Measure and Area Under the ROC Curve (AUC).