Abstract The article uses the law of large numbers to analyze and equivalence relationship on data set measure degree uncertainty knowledge. In this way, we can between employment graduates from colleges universities professional education training. describes several collective category models college graduates’ rate enrollment scale. This provide an important theoretical reference for solving ...

This paper generalizes the concept of rough membership functions in pattern classi$cation tasks to rough–fuzzy membership functions and rough–fuzzy ownership functions. Unlike the rough membership value of a pattern, which is sensitive only towards the rough uncertainty associated with the pattern, the rough–fuzzy membership (or ownership) value of the pattern signi$es the rough uncertainty as ...

Rights-based approaches (e.g., ITQs, TURFs, collectives/cooperatives) are increasingly popular throughout the world, but there is relatively little theoretical literature comparing individual vs. collective approaches. This paper presents a stylized model that allows for such a comparison in a context where there is harvest uncertainty (and hence the potential for risk sharing) as well as poten...

0377-2217/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.ejor.2010.08.017 ⇑ Corresponding author. Tel.: +886 2 27883799. E-mail addresses: [email protected] (T.-F. Fan), Liau), [email protected] (D.-R. Liu). Attribute reduction is very important in rough set-based data analysis (RSDA) because it can be used to simplify the induced decision rules without reducing the classification accu...

Decision-theoretic rough set theory is quickly becoming a research direction in rough set theory, which is a general and typical probabilistic rough set model with respect to its threshold semantics and decision features. However, unlike the Pawlak rough set, the positive region, the boundary region and the negative region of a decision-theoretic rough set are not monotonic as the number of att...

The ability to learn from empirical data or the observation of a real world and accurately predict future instances is an important feature expected in human. It allows knowledge to be gained by experience and decision rules induced from empirical data. One of the major obstacles in performing rule induction from empirical data is the inconsistency of information about a problem domain. Rough s...

The demand for Internet content rose dramatically in recent years. Servers became more and more powerful and the bandwidth of end user connections and backbones grew constantly during the last decade. Nevertheless users often experience poor performance when they access web sites or download files. Reasons for such problems are often performance problems, which occur directly on the servers (e....

Probabilistic planners are very flexible tools that can provide good solutions for difficult tasks. However, they rely on a model of the domain, which may be costly to either hand code or automatically learn for complex tasks. We propose a new learning approach that (a) requires only a set of state transitions to learn the model; (b) can cope with uncertainty in the effects; (c) uses a relation...