Association rule mining (ARM) problem has been extensively tackled in the context of perfect data. However, real applications showed that data are often imperfect (incomplete and/or uncertain) which leads to the need of ARM algorithms that process imperfect databases. In this paper we propose a new algorithm for mining frequent itemsets from evidential databases. We introduce a new structure ca...

Intuitions play a critical role in analytical philosophical activity. But do they qualify as genuine evidence for the sorts of conclusions philosophers seek? Skeptical arguments against intuitions are reviewed, and a variety of ways of trying to legitimate them are considered. A defense is off ered of their evidential status by showing how their evidential status can be embedded in a naturalist...

Mining database provides valuable information such as frequent patterns and especially associative rules. The associative rules have various applications and assets mainly data classification. The appearance of new and complex data support such as evidential databases has led to redefine new methods to extract pertinent rules. In this paper, we intend to propose a new approach for pertinent rul...

Uncertain databases are used in some fields to store both certain and uncertain data. When uncertainty is represented with the theory of belief functions, uncertain databases are assumed to be evidential. In this paper, we suggest a new method to quantify the source degree of dependence in order to enrich its evidential database by adding this dependence information. Enriching evidential databa...

Abstract We put forth an account for when to believe causal and evidential conditionals. The basic idea is embed a model in agent’s belief state. For the evaluation of conditionals seems be relative beliefs about both particular facts relations. Unlike other attempts using models, we show that ours can rather well not only various but also

The Conditional Neural Process (CNP) family of models offer a promising direction to tackle few-shot problems by achieving better scalability and competitive predictive performance. However, the current CNP only capture overall uncertainty for prediction made on target data point. They lack systematic fine-grained quantification distinct sources that are essential model training decision-making...