[Cite as http://purl.org/au-research/grants/arc/DP170101632]
Researchers Prof Jie Lu; A/Prof Guangquan Zhang; Prof Dr Witold Pedrycz
Brief description This project aims to develop a set of cross-domain knowledge transfer methodologies to support Data-Driven Decision-Making (D3M) systems. D3M is essential in business, particularly for ever-changing environments in today’s big data era, but D3Ms for solving new problems may face in-domain data insufficiency. The challenge is to effectively transfer knowledge from multiple heterogeneous source domains. The outcomes are expected to transfer implicit and explicit knowledge, handle discrete and continuous outputs, and support business decision-making, which should advance the discipline of transfer learning and data-driven DSS in dynamically changing environments.
Funding Amount $381,000
Funding Scheme Discovery Projects