An Optimized Human-Machine Intelligence Framework for Classification

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 crowd sourcing, 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. 

Senjuti Basu Roy
senjutib@njit.edu
https://web.njit.edu/~senjutib/index.html