In this paper we consider minimax games for stochastic uncertain systems with the pay-off being a nonlinear functional of the uncertain measure where the uncertainty is measured in terms of relative entropy between the uncertain and the nominal measure. The maximizing player is the uncertain measure, while the minimizer is the control which induces a nominal measure. Existence and uniqueness of...

In recent years, uncertain data clustering has become the subject of active research in many fields, for example, pattern recognition, and machine learning. Nowadays, researchers have committed themselves to substitute traditional distance or similarity measures with new metrics existing centralized algorithms order tackle uncertainty data. However, perform clustering, representation plays an i...

Shortest path problem is a fundamental problem in network optimization and combinational optimization. The existing literature mainly concerned with the problem in deterministic, stochastic or fuzzy environments by using different tools. Different from the existing works, we investigate shortest path problem by regarding arc lengths as uncertain variables which are employed to describe the beha...

Semi-absolute deviation is a commonly used downside risk measure in the portfolio optimization problem. However, there is no literature on taking semi-absolute deviation as a risk measure in the framework of uncertainty theory. This paper fills the gap by means of defining semi-absolute deviation for uncertain variables and establishes the corresponding mean semi-absolute deviation models in un...

Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts' evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns....

Decision tree is powerful and popular tool for classification and prediction in uncertainty data. This study proposes a decision tree based classification system for uncertain data. The uncertain data means lack of certainty. Data uncertainty comes by different parameters including sensor error, network latency measurements precision limitation and multiple repeated measurements.It is found tha...

Conventional approaches to similarity search and case-based retrieval, such as nearest neighbor search, do require the specification of a global similarity measure which is typically expressed as an aggregation of local measures pertaining to different aspects of a case. Since the proper aggregation of local measures is often quite difficult, we propose a novel concept called similarity skyline...

In this paper we propose a new method for classifying uncertain data, modeled as interval-valued fuzzy sets. We develop the notion of an interval-valued prototype-based fuzzy classifier, with the idea of preserving full information including the uncertainty factor about data during the classification process. To this end, the classifier was based on the uncertainty-aware similarity measure, a n...

Conventional approaches to similarity search and case-based retrieval, such as nearest neighbor search, require the specification of a global similarity measure which is typically expressed as an aggregation of local measures pertaining to different aspects of a case. Since the proper aggregation of local measures is often quite difficult, we propose a novel concept called similarity skyline. R...

Due to the inaccuracy and noisy, uncertainty is inherent in time series streams, and increases the complexity of streams clustering. For the continuous arriving and massive data size, efficient data storage is a crucial task for clustering uncertain data streams. With hash-compressed structure, an extended uncertain sketch and update strategy are proposed to store uncertain data streams. And ba...