Active Learning in Machine Learning: What It Is and How To Use It

With data becoming cheaper to collect and store, data scientists are often left overwhelmed by the sheer volume of unlabeled data. Active learning (machine learning) helps them make sense of it all. 

Algorithms are typically used to actively select the data the machine is learning from and training on. The machine learns and can choose from a pool of unclassified data to label as similar to the training data. It can then continually train on this incoming data.

The overall goal of active learning as part of machine learning is to minimize how much labeled data the machine needs to train on, while maximizing its overall performance moving forward. That’s why data scientists use active learning tools to enhance machine learning, annotating, and labeling data used in the training stage. 

Active learning ML: How does it work?

Active learning generally operates through an interactive loop-based process. Here's what the process of active learning in machine learning looks like.

• Initialization. At this first stage, a small set of pre-labeled data points are input into the system to begin training the machine. It’s essential to get this step right, as it forms the basis for how the machine understands what data to label and train on in future iterations.
• Model training. Once input is complete, the model can begin its training with the labeled data. 
• Query strategy. When the initial training is complete, the query strategy guides the machine in selecting which new data to label next. 
• Human annotation. Some data points may need to be assessed and annotated by a human data scientist, especially during initial rounds. This ensures the data is parsed correctly and labeled appropriately for ongoing training. Mistakes at this stage can significantly alter how the machine trains, so it’s important to have human input here.
• Model update. After the new data is labeled and incorporated into the training set, the model can retrain with this new, enhanced data to improve the overall outcome.
• Active learning loop. Steps 3 through 6 are repeated to allow the machine to continually select the most informative data. This enables the algorithm to label and add this to the training dataset. When new data no longer provides significant improvements or another stopping point is determined, the training will end, and the machine will be ready to use.

Source: Thoughtworks

Active learning query strategies 

We've learned that active learning enhances model training by selecting the most valuable data points from an unlabeled dataset. This process of selecting data points, or query strategy, can be categorized into the following three methods.

Stream-based selective sampling

It's active learning when data arrives continuously, like in real-time analysis. The model processes data one piece at a time and selects the most useful samples for labeling to improve its accuracy. Two common strategies for selection are:

• Uncertainty sampling: Picking samples the model is unsure about.
• Diversity sampling: Choosing samples that are different from what the model has seen.

This approach is great for live scenarios, like analyzing video streams, where waiting for a batch of data isn’t possible. It saves labeling costs, adapts to changing data, and scales well. However, it can face challenges like bias, selecting less helpful samples, and relying on the streaming setup.

Pool-based sampling

With this method, the model selects the most valuable data points from a pool of unlabeled data for labeling, focusing only on examples that can improve its accuracy. Pool-based sampling saves time, cost, and resources and accelerates learning by targeting the most informative samples. However, its effectiveness depends on the quality of the unlabeled data pool and the sampling strategy. Poorly selected data or ineffective methods can lower model performance, and it may not work well with unstructured or noisy data. Also, due to the size of datasets, it often requires substantial digital memory.

Query synthesis methods

Query synthesis methods are techniques used in active learning to generate new samples for labeling from existing data. This approach is useful when labeled data is limited or expensive to obtain. By creating diverse training data, these methods help improve the model’s performance. Here's what to do:

• Perturbation: Making slight changes to existing labeled data, such as adding noise or flipping labels.
• Interpolation/extrapolation: Combining or extending existing samples to create new ones.
• Generative methods: Using techniques like generative adversarial networks (GANs) to synthesize realistic data.

These synthetic samples are labeled by an annotator and added to the training dataset, providing the model with more representative and diverse training data.

Some limitations of this approach include:

• High computational cost when generating synthetic samples, especially for complex data like images or videos.
• Reduced accuracy from poorly designed methods that produce unrepresentative data.
• The risk of overfitting, where the model may prioritize synthetic data over real-world data.

Active learning vs. passive learning

When training machine learning models, the approach to data labeling and selection plays a crucial role in determining efficiency and performance. Active learning and passive learning are two distinct strategies used for this purpose. The table below highlights the key differences between these approaches:

Active learning vs. reinforcement learning

Both active learning and reinforcement learning are focused on reducing the amount of labels needed to develop a model but operate from different perspectives.

Active learning

As discussed before, this technique selects the most valuable samples from an unlabeled dataset and queries a human annotator for their labels. It enhances the model's accuracy while keeping labeling costs low. Active learning is particularly beneficial in areas like medical imaging and natural language processing (NLP), where labeling can be expensive and time-consuming.

Reinforcement learning

Reinforcement learning, on the other hand, focuses on training an agent to make a series of decisions within an environment. The agent learns by interacting with the environment and receiving feedback through rewards or penalties based on its actions. This method is commonly applied in robotics and autonomous systems. Reinforcement learning aims to maximize cumulative rewards over time, encouraging the agent to explore and optimize its actions to achieve long-term objectives.

Benefits of active learning model

There are several key benefits to active learning within machine learning, largely focused on speed and costs for data scientists.

Reduces labeling costs 

Large datasets take up significant memory and are expensive to parse and label. By reducing the amount of data being labeled, active learning can significantly minimize budget outgoings. Auto-segmentation rules can also help keep costs down while ensuring that the data being used is the most significant for the expected outcome.

Faster convergence 

Convergence is a vital part of machine learning. During training, the model settles losses to a point where additional training won’t improve the model any further. Active learning helps reach this point of convergence faster by focusing only on the most relevant data samples.

Greater accuracy 

Using the most informative samples for labeling, accuracy can be achieved faster and improve the model’s performance. Active learning models are designed to choose the data samples that reduce the model’s uncertainty, while aiming for greater accuracy over time. 

Active learning ML use cases

Active learning finds applications across various domains. Here are a few examples:

• NLP: Active learning is used for tasks like sentiment analysis, named entity recognition, and text classification, where manually labeling text data can be labor-intensive. By focusing on the most ambiguous or novel sentences, active learning reduces labeling costs.
• Medical diagnosis: In medical imaging and diagnostics, active learning helps identify the most informative cases for experts to review, thus, enhancing the model's ability to make accurate predictions with less labeled data.
• Speech recognition: Active learning helps develop speech models by efficiently labeling speech data. However, this process can be challenging and expensive due to the need for linguistic expertise.
• Fraud detection: In financial services, active learning can be used to identify potentially fraudulent transactions that are atypical or ambiguous, enabling more effective use of human oversight.
• Autonomous vehicles: Active learning assists in training models by selecting edge cases from real-world driving data that are critical for improving the safety and performance of autonomous systems.
• Drug discovery: Active learning is applied in the process of selecting chemical compounds that are worth investigating further, which is feasible through minimizing the number of experiments needed.
• Image classification: In scenarios where labeling images is costly or time-consuming, active learning can be employed to select the most uncertain or representative images for labeling, improving model performance without needing to label the entire dataset.

Make active learning your default training model

Using active learning techniques to train your AI models is one of the best ways to save money on large machine learning projects while speeding up iteration times before reaching crucial convergence levels. Explore new technology and develop your models into usable, useful projects with these techniques!

Don't have the necessary resources in-house? Check out machine learning as a service (MLaaS) for model training and development.