Decision making via semi-supervised machine learning techniques
This incremental approach benefits practical applications like industrial monitoring and surveillance by making systems cost-effective and adaptable.
The paper tackles the problem of reducing labeling costs and handling large datasets in decision support systems by using semi-supervised learning, which requires minimal labeled data and adapts to new conditions with improved performance.
Semi-supervised learning (SSL) is a class of supervised learning tasks and techniques that also exploits the unlabeled data for training. SSL significantly reduces labeling related costs and is able to handle large data sets. The primary objective is the extraction of robust inference rules. Decision support systems (DSSs) who utilize SSL have significant advantages. Only a small amount of labelled data is required for the initialization. Then, new (unlabeled) data can be utilized and improve system's performance. Thus, the DSS is continuously adopted to new conditions, with minimum effort. Techniques which are cost effective and easily adopted to dynamic systems, can be beneficial for many practical applications. Such applications fields are: (a) industrial assembly lines monitoring, (b) sea border surveillance, (c) elders' falls detection, (d) transportation tunnels inspection, (e) concrete foundation piles defect recognition, (f) commercial sector companies financial assessment and (g) image advanced filtering for cultural heritage applications.