Background Positive unlabeled (PU) learning is a semi-supervised machine learning approach to a binary classification problem in which most of the data is unlabeled. PU learning algorithms reduce the computational cost of training machine learning classifiers, because the labeling of data is time-intensive for supervised machine learning algorithms....

Unlike traditional semi-supervised learning, positive unlabeled learning requires only some labeled data from the positive class. All other data, both positive and negative, is unlabeled. The goal is the same, i.e., to construct a classification model that correctly labels unlabeled images and create a model to label future images. This problem is...

Despite substantial improvements in solar array efficiency in recent years, accurate fault detection and diagnosis remains an open problem as undetected faults can cause substantial power loss and hazardous conditions. Solar panel arrays can experience several types of faults of varying severity. Some faults, such as those associated with soiled, dirty,...