Region-Based Active Learning with Hierarchical and Adaptive Region Construction.

Area of Science:

• Machine Learning
• Data Science
• Computer Vision

Background:

• Traditional classification models require extensive human annotation, which is costly and time-consuming.
• Reducing annotation effort is crucial for practical model development.
• Instance-based labeling presents a bottleneck in supervised learning.

Purpose of the Study:

• To explore region-based annotation as an alternative to instance-based labeling for reducing annotation costs.
• To develop an active learning framework for efficient discovery of pure data regions.
• To demonstrate the effectiveness of the proposed method with limited human feedback.

Main Methods:

• Developed a novel active learning framework utilizing hierarchical and adaptive region construction.
• Regions are defined as hyper-cubic subspaces of the input space, representing subpopulations.
• Regions are labeled with estimated class proportions, guiding the learning process.

Main Results:

• The proposed framework successfully identifies pure regions within the data.
• Experiments show that the method requires very few region queries to achieve its goals.
• Effective learning of classification models was demonstrated with significantly reduced human feedback.

Conclusions:

• Region-based annotation is a viable and cost-effective alternative to instance-based annotation.
• The hierarchical and adaptive active learning framework efficiently reduces annotation burden.
• This approach enables the development of classification models with minimal human input.