By combining the explicit formula of the Shannon informational entropy ) , ( Y X H for two random variables X and Y , with the entropy )) ( ( X f H of ) (X f where (.) f is a real-valued differentiable function, we have shown that the density of the amount of information in Shannon sense involved in a non-random differentiable function is defined by the logarithm of the absolute value of its de...

A novel ranking method based on multi-time information fusion is proposed for intuitionistic fuzzy sets (IFSs) and applied to the threat assessment problem, a multi-attribute decision making (MADM) one. This method integrates a designed intuitionistic fuzzy entropy (IFE), the closeness degree of technique for order preference by similarity to ideal solution (TOPSIS), the decision maker¡¯s (DM¡¯...

One of the primary concerns in any system design problem is to prepare a highly reliable system with minimum cost. One way to increase the reliability of systems is to use redundancy in different forms such as active or standby. In this paper, a new nonlinear multi- objective integer programming model with the choice of redundancy strategy and component type is developed where standby strategy ...

In this paper, we propose a method for learning a classifier which combines outputs of more than one Japanese named entity extractors. The proposed combination method belongs to the family of stacked generalizers, which is in principle a technique of combining outputs of several classifiers at the first stage by learning a second stage classifier to combine those outputs at the first stage. Ind...

The existence of noise has great influence on the real features of observed time series, thus noise reduction in time series data is a necessary and significant task in many practical applications. When using traditional de-noising methods, the results often cannot meet the practical needs due to their inherent shortcomings. In the present paper, first a set of key but difficult wavelet de-nois...

A long standing mystery in using Maximum Entropy (MaxEnt) is how to deal with constraints whose values are uncertain. This situation arises when constraint values are estimated from data, because of finite sample sizes. One approach to this probiem, advocated by E.T. Jaynes [1], is to igriore this uncertainty, and treat the empirically observed values as exact. We refer to this as the classic M...