Although active learning has many similarities with semi-supervised learning, it has its own distinctive approach to modeling with datasets containing labeled and unlabeled data. It has roots in the basic human psychology that asking more questions often tends to solve problems.
The main idea behind active learning is that if the learner gets to pick the instances to learn from rather than being handed labeled data, it can learn more effectively with less data (Reference [6]). With very small amount of labeled data, it can carefully pick instances from unlabeled data to get label information and use that to iteratively improve learning. This basic approach of querying for unlabeled data to get labels from a so-called oracle—an expert in the domain—distinguishes active learning from semi-supervised or passive learning. The following figure illustrates the difference and the iterative process involved:
Figure 7. Active Machine Learning process contrasted with Supervised and Semi...