The goal of this project is to develop an optimized human-machine intelligence framework for single and multi-label classification problems through active learning. The focus is on citizen science based cyber-human systems that collect species observation information for ecological applications, but the findings will apply more broadly. It will adapt several existing active learning techniques for single and multi-label classification, but study them in the context of crowdsourcing, especially considering worker-centric optimization. The innovations lie in systematically characterizing variables to model human factors, designing optimization models that appropriately combine system and worker-centric goals, and discovering innovative solutions. The project describes an iterative framework that judiciously employs human workers to collect labels, which, in turn, are used by the supervised machine algorithms to make intelligent predictions. The framework derives its principles by appropriately combining active learning and human factors modeling strategies to judiciously select tasks and workers for single and multi-labels classification problems. The key intellectual contributions are: 1. Novel worker-centric optimization models considering variables pertinent to workers and tasks through human factors modeling. 2. Optimized human-machine intelligence models for single-label classification through active learning. 3. Optimized human-machine intelligence models for multi-labels classification through active learning. 4. Innovative solutions for human factors estimation, task assignment for single and multi-labels tasks. 5. Real world deployment and rigorous evaluation in the citizen science domain. The project is likely to impact multiple disciplines and domain experts, such as the works of naturalists, ecologists, conservation biologists and citizen scientists.This award reflects National Science Foundation 's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
|Effective start/end date||9/1/18 → 8/31/21|
- National Science Foundation