Active Hybrid Classification
This addresses classification problems where combining cost-effective automation with human accuracy is beneficial, though it appears incremental as it builds on existing hybrid methods.
The paper tackles the learning vs. exploitation trade-off in hybrid crowd-machine classification with finite item pools, proposing an architecture that orchestrates active learning and crowd classification in a cycle. It shows the approach significantly outperforms baselines on three real-world datasets.
Hybrid crowd-machine classifiers can achieve superior performance by combining the cost-effectiveness of automatic classification with the accuracy of human judgment. This paper shows how crowd and machines can support each other in tackling classification problems. Specifically, we propose an architecture that orchestrates active learning and crowd classification and combines them in a virtuous cycle. We show that when the pool of items to classify is finite we face learning vs. exploitation trade-off in hybrid classification, as we need to balance crowd tasks optimized for creating a training dataset with tasks optimized for classifying items in the pool. We define the problem, propose a set of heuristics and evaluate the approach on three real-world datasets with different characteristics in terms of machine and crowd classification performance, showing that our active hybrid approach significantly outperforms baselines.