Semi-Supervised Learning: What is it?

Semi-supervised learning is the middle ground between supervised and un-supervised machine learning. For context, supervised machine learning requires labeled datasets for predicting output and un-supervised machine learning works with un-labeled datasets. Both of them have their pros and cons. On one end of the spectrum, supervised learning is more accurate, but it has limited applications and requires more cost and resources for manual labelling of the data, while on the other end, un-supervised learning has wider applications with less cost, but theresultss accuracy is compromised.

Semi-supervised learning therefore bridges the gap between both these types of machine learning. It is the type of machine learning that involves the use of both labeled and unlabeled datasets. However, the labelled dataset is present in considerably less quantity than the unlabeled one. A greater amount of labelled data means less supervision from humans.

This is how semi-supervised learning aims to generate the best possible results without compromising accuracy and resources. 

Working of Semi-Supervised Learning

Semi-supervised learning works with three assumptions:

1. Continuity assumption – assumes that the closer the points, the likelier they have the same output label.
2. Manifold assumption – assumes that although the dataset is of a higher dimension, necessary data points can be found on the manifold of lower dimension.
3. Cluster assumption – assumes that data points in the same cluster have a higher chance of having the same output label.

Semi-supervisedd learning can perform un-supervised learning tasks like clustering and association and supervised learning tasks like regression and classification. It follows the following basic steps:

• First, the model is trained under human supervision with labelled data. 
• Next, the model is trained with unlabeled data with the help of pseudo labels which may generate some errors or false results. 
• Both training courses are then linked together by connecting their labels and input data. 
• The model is trained again with the combined data.

Types of Semi-Supervised Learning

Semi-supervised learning is broadly divided into two types:

Self-Training

The model is trained with a small set of labelled data. That trained model is then used on unlabeled data and the results are observed. The correct predictions are added to the labeled dataset and the training process of the model is repeated. In this way, the unlabeled data is labelled with the help of olabelleded data.

Co-Training

Different classifiers are assigned to specific featuresubsetst of the original dataset. These classifiers are trained on a small set of labelled data first and then used on unlabeled data. The outputs from all the classifiers are combined and the agreement between these classifiers is used to select the accurately predicted labels and train the model again.

Real-Time Applications

One of the many real-life applications of semi-supervised learning is medical imaging in healthcare organizations. The database in the organization may contain a large number of images such as X-rays, CT scans MRI scans and other diagnostic images. Some of these images may be labeled while most of them may not. Semi-supervised learning can be used here to use the labelled images to train the initial model and the generic pseudo labels for the unlabeled images. The first run may produce incorrect results but iterating the procedure will lead to increased accuracy. The final trained model can then be used to classify the images and predict accurate diagnosis and treatment planning in medical imaging applications.

This approach ultimately is benefitting for the patients, doctors and medical professionals involved because so much time and resources are saved with the help of semi supervised machine learning.

Apart from medical diagnosis, semi supervised learning is also seen being used in:

• Recommendation systems
• Image classification
• Speech recognition
• Fraud detection
• Document classification
• Drug discovery
• Bioinformatics analysis
• Autonomous vehicle
• Supply chain optimization
• Climate modelling

In today’s data-driven world, a vast array of information spanning diverse domains is amassed daily. Leveraging this wealth of data intelligently entails analyzing it meticulously to discern patterns and derive actionable insights. This approach not only optimizes strategies but also bolsters profitability while mitigating the risks of potential losses for businesses and organizations. For the analysis and interpretation of this data, semi-supervised machine learning is a potent tool with extensive applications.