Entropy is commonly used as a way to measure the uncertainty of random variables. In uncertain set theory, a concept of entropy for uncertain sets has been defined by using logarithm. However, such an entropy fails to measure the uncertain degree of some uncertain sets. This paper aims at proposing a concept of elliptic entropy for uncertain sets, and investigating its properties such as transl...

We present V-measure, an external entropybased cluster evaluation measure. Vmeasure provides an elegant solution to many problems that affect previously defined cluster evaluation measures including 1) dependence on clustering algorithm or data set, 2) the “problem of matching”, where the clustering of only a portion of data points are evaluated and 3) accurate evaluation and combination of two...

We use information entropy measure to extend the rough set based notion of a reduct. We introduce the Approximate Entropy Reduction Principle (AERP). It states that any simplification (reduction of attributes) in the decision model, which approximately preserves its conditional entropy (the measure of inconsistency of defining decision by conditional attributes) should be performed to decrease ...

purpose: this study aimed to measure the information load of words in farsi scientific texts and determine the relationship between some properties of the words and information load of the texts based on shannon entropy. methodology: the study was conducted based on the content analysis of a number of 320 articles that were published in scientific-research iranian journals in 2009. and the arti...

In arbitrage-free but incomplete markets, the equivalent martingale measure Q for pricing traded assets is not uniquely determined. A possible approach when it comes to choosing a particular pricing measure is to consider the one that is ‘closest’to the physical probability measure P, where closeness is measured in terms of relative entropy. In this paper, we determine the minimal entropy marti...