Decision making via semi-supervised machine learning techniques

TL;DR

This paper discusses semi-supervised learning techniques that leverage unlabeled data to improve decision support systems across various practical fields, reducing labeling costs and adapting to new conditions efficiently.

Contribution

It introduces the application of semi-supervised learning to decision support systems, highlighting its benefits in cost reduction and adaptability in dynamic environments.

Findings

SSL reduces labeling costs significantly.

SSL enables continuous system adaptation with minimal labeled data.

Applicable to diverse fields like industrial monitoring and cultural heritage.

Abstract

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.