Traditional machine learning algorithms assume that data are exact or precise. However, this assumption may not hold in some situations because of data uncertainty arising from mea...
Jiangtao Ren, Sau Dan Lee, Xianlu Chen, Ben Kao, R...
Hierarchical models have been extensively studied in various domains. However, existing models assume fixed model structures or incorporate structural uncertainty generatively. In...
In this paper we present a procedure to deal with a kind of single-stage decision problems with imprecise utilities. In this type of problems the product measurability of the utili...
Abstract-- The inherent uncertainty of data present in numerous applications such as sensor databases, text annotations, and information retrieval motivate the need to handle impre...
Sarvjeet Singh, Chris Mayfield, Rahul Shah, Sunil ...
Data integration in medical applications is a crucial and sensitive task. It turns out that there are rigid factors like heterogeneous, distributed data sources, security, and comp...