Uncertain hypothesis test is a statistical tool that uses uncertainty theory to determine whether some hypotheses are correct or not based on observed data. As an application of uncertain test, this paper proposes method differential equation fits the data not. In order demonstrate method, numerical examples provided. Finally, both currency model and stochastic used US Dollar Chinese Yuan (USD–...

Recommender systems have been proven to be valuable means for Web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. The suggestions provided are aimed at supporting their users in various decision-making processes, such as what items to buy, what music to listen, or what news to read. In the paper, we introduce ...

Abstract This special issue of Cadernos EBAPE.BR sought to provoke a reflection focused on uncertain consumption practices in an future. While prior marketing and consumer research has explored framed by institutionalized, known, more or less predictable context, the set articles approved this edition reveals different nuances. From experiential accounts grief role music coping with struggles i...

On the basis of the possibility measure, necessity measure, credibility measure and probability measure, chance-constrained programming models are designed to treat optimization problems with stochastic and fuzzy parameters. Then, mathematical properties of different models, for instance, crisp equivalents of uncertain functions and constraints, are discussed on condition that parameters are un...

: Clustering on uncertain data, one of the essential tasks in data mining. The traditional algorithms like K-Means clustering, UK Means clustering, density based clustering etc, to cluster uncertain data are limited to using geometric distance based similarity measures and cannot capture the difference between uncertain data with their distributions. Such methods cannot handle uncertain objects...

Clustering on uncertain data, one of the essential tasks in mining uncertain data, posts significant challenges on both modelling similarity between uncertain objects and developing efficient computational methods. The existing methods extend traditional partitioning clustering methods like k-means and density-based clustering methods like DBSCAN and Kullback-Leibler to uncertain data, thus rel...

Introduction In order to indicate human’s belief degree on the chance that an event happens, an uncertain measure was defined by Liu [1, 2] as a set function satisfying normality, duality, subadditivity and product axioms. Uncertain set, initialized by Liu [3], is a set-valued function on an uncertainty space to model the unsharp concepts such as “old people” and “heavy vehicles”. In order to d...

This paper considers a scheduling problem with uncertain processing times and machine breakdowns in industriall/office workplaces and solves it via a novel robust optimization method. In the traditional robust optimization, the solution robustness is maintained only for a specific set of scenarios, which may worsen the situation  for new scenarios. Thus, a two-stage predictive algorithm is prop...

We uncover relations between robust MDPs and risk-sensitive MDPs. The objective of a robust MDP is to minimize a function, such as the expectation of cumulative cost, for the worst case when the parameters have uncertainties. The objective of a risk-sensitive MDP is to minimize a risk measure of the cumulative cost when the parameters are known. We show that a risk-sensitive MDP of minimizing t...