Elevating Machine Learning Performance with Active Learning
Active learning in machine learning (ML) helps businesses fully utilize ML algorithms even though they lack labeled data.
Many organizations have an abundance of unlabeled data that needs to be manually manipulated before an ML solution can digest it. This manual labeling is expensive.
Active learning (or active machine learning) reduces labeling costs and effort while significantly improving ML models.
It accomplishes this by using a learning algorithm that queries a human user to label a representative set of data points. It then takes those labels and applies them to the rest of the data.
This article explores how active learning works and benefits machine learning initiatives.
Active Learning vs. Passive Learning
Before getting into active learning, we have to talk about passive machine learning.
The difference is essentially the level of human involvement in labeling data. Data comes in two parts: the features or raw input (images, audio signals, sensor data, etc.) and the labels, which usually have to be provided by humans.
Passive learning starts with a large pool of unlabeled raw data, which a human expert manually labels. If the data pool is very large (millions of records), it may take several hundred people. That labeled training set is then turned over to a machine learning algorithm, which produces a predictive model.
For example, imagine the raw data is a collection of unlabeled animal images. A human or group of humans goes through and labels them dogs, cats, cows, horses, and so on. The more images in the collection, the longer it takes to label them all manually.
The algorithm can only produce the predictive model after the human is finished, making the human the bottleneck in the process. Active learning doesn’t eliminate human intervention, but it reduces the dependence on humans.
What Is Active Learning?
Active learning in machine learning is an approach where the algorithm interactively queries a human annotator to label the most informative data points. The key idea is strategically selecting which data to label, rather than passively using a pre-labeled dataset.
Tackling the same collection of animal images with active learning reduces the involvement of the human expert and makes them more of a supervisor or “oracle.” Instead of labeling every image in the database, they only label a representative sample—maybe only 10 percent of the images.
Once that’s done, the machine learning algorithm uses that sample to label the rest of the images. If it has low confidence in an image, it asks the human oracle to label that image. The model is then retrained on the newly labeled data. This process is repeated until the desired level of performance is reached.
Key Concepts of Active Learning
The following concepts play crucial roles in training the machine learning model with active learning.
Ground Truth Labels
Ground truth labels refer to the actual, correct outputs or annotations associated with a dataset. They represent the true and accurate labels for the data. Ground truth labels serve as the reference or benchmark against which machine learning models are trained and evaluated.
In active machine learning, ground truth labels are used to teach algorithms how to make predictions by minimizing the difference between model outputs and the true labels. They are crucial for assessing model performance. After training, models are evaluated by comparing their predictions to the ground truth labels to identify errors or inconsistencies.
Model Uncertainty
Model uncertainty is used to identify the most informative or valuable samples for labeling. The thinking here is that samples the model is most uncertain about are likely to be the most informative for improving model performance. It can potentially reduce the amount of labeled data needed for training, and it helps identify decision boundaries and challenging examples.
However, deep learning models can sometimes be poor at estimating their own uncertainty. Pure uncertainty-based sampling may lead to selecting outliers or very similar samples. Different uncertainty measures may be more appropriate for different tasks or model types.
Oracle
The oracle is typically a human expert or an authoritative information source that can provide accurate labels for the data points queried by the active learning system. The oracle’s primary role is to annotate or label the most informative samples selected by the algorithm.
Oracles possess knowledge or expertise in the problem domain, allowing them to provide accurate labels. Ideally, the oracle should provide consistent labels for similar data points and be available to respond quickly to queries from the active learning system.
Query Strategy
A query strategy is a method used to select the most informative unlabeled data points for labeling. Query strategies seek to identify which unlabeled samples, if labeled, would improve the model’s performance most.
An effective query strategy helps achieve high model performance with minimal labeling effort, making the learning process more efficient and cost-effective.
Stopping Conditions
Stopping conditions determine when to end the active learning process. Defining appropriate stopping criteria helps balance model performance with labeling costs. The stopping condition may be when the model’s performance plateaus, a target accuracy is met, or a fixed number of labeled examples is reached.
Benefits of Active Learning
The benefits of active learning are not difficult to see. Automating the data labeling process limits the amount of human effort needed, and human effort is expensive.
So, the first benefit is the cost saved by reducing the amount of labeled data required for training. At the same time, active learning often produces more accurate models because it concentrates on the most instructive examples.
Active learning also optimizes resource utilization by eliminating the human bottleneck in data labeling.
And because active learning allows the model to concentrate on complex examples, it works well in situations where data distribution is uneven or unbalanced.
Leveraging Active Machine Learning
Active learning is a powerful technique for improving machine learning performance while reducing labeling costs. By strategically selecting the most informative samples, you can build more accurate models with less human effort.
However, leveraging active machine learning requires carefully selecting a query strategy and maintaining a smooth workflow between your model and human oracles.
If you need help with active learning in machine learning or incorporating AI into your business, contact Taazaa’s AI development services team. Start taking advantage of AI and machine learning to improve your business operations.
