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In this paper, a new entropy based uncertainty measure is introduced for evaluating the significance of subsets of attributes in incomplete decision tables. Some properties of rough conditional entropy are derived. And three attribute reduction algorithms are provided, including an algorithm using exhaustive search, an algorithm using heuristic search and an algorithm using probabilistic search for...
In the light of the parameters interrelated characteristics in the process of chaos phase space reconstruction, this paper proposed a new way to determine embedding dimension and delay time simultaneously, that is, symbolic analysis-based multi-component conditional entropy method. Conditional entropy was introduced to characterize information increment and served as the measurement of complexity...
We are interested in inferring the set of waypoints (or intermediate destinations) of a mobility trajectory in the absence of timing information. We find that, by mining a dataset of real mobility traces, computing the entropy of conditional Markov trajectory enables us to uncover waypoints, even though no timing information nor absolute geographic location is provided. We build on this observation...
This paper discusses in depth the different parts of the MIR-max clustering algorithm with respect to the problem of diagnosing river quality. An equivalent information theoretic measure is proposed in this paper for clustering which is based on conditional entropy. The original mutual information method of clustering is compared with the proposed conditional entropy of states given to the cluster...
This paper presents a novel active-learning (AL) technique in the context of the cascade classification of multitemporal remote-sensing images for updating land-cover maps. The proposed AL technique is based on the selection of unlabeled samples that have maximum uncertainty on their labels assigned by cascade classification, and explicitly exploits temporal correlation between multitemporal images...
Rough set theory is an important technique for knowledge discovery in databases. The measurement of the uncertainty of knowledge is one of the important issues in rough set theory. The definitions of entropy and the conditional entropy in the process of probability are given, and the meanings of entropy and the conditional entropy are explained in this paper. In addition, the new definition of the...
The paper analyzes the limitations from the angles of conditional entropy and the expression of information. It is pointed out Shannon's definition of information is not absolute and the probability in the expression of information maybe random variable in practice, but in information theory the probability is treated as a fixed value, then the application of Shannon's theory is limited. It is stated...
We consider a secure lossless source coding problem with a rate-limited helper. In particular, Alice observes an i.i.d. source Xn and wishes to transmit this source loss-lessly to Bob at a rate Rx. A helper, say Helen, observes a correlated source Yn and transmits at a rate Ry to Bob. A passive eavesdropper can observe the coded output of Alice. The equivocation ?? is measured by the conditional entropy...
The problems of conditional entropy's definition and the formula to compute conditional entropy are analyzed from various perspectives. Examples are given to prove the conclusion that conditional entropy never be increased is not absolute, thus the representation that information is to decrease uncertainty in the definition of information is not absolutely correct. In most conditions the reduction...
This paper presents a method based on information entropy to analyze the capacity of covert channels. Built upon noninterference, the method is firstly used to calculate the covert channel capacity, then to analyze the factors that have an impact on this quantity. Finally an example is given of the capacity calculation for covert channels in a multilevel secure database.
Uncertainty measure is a key issue of uncertain systems. In this paper, conditional entropy and increment information are introduced to discuss the two set relations in the same incomplete information systems based on covering approximation space. Two new kinds of measurements about the knowledge of rough set are presented. Furthermore the finer the set is, the lower the conditional entropy and increment...
The paper in the first place reviews the information and the uncertainty measures of joint and marginal probability distributions of the sets and subsets of random events. Next it reminds on the relations of the unconditional and conditional entropy of joint and marginal distributions and their combinations. Then it elaborates the ways how these measures can be applied in the sea surface uncertainty...
Partition entropy is the numerical metric of uncertainty within a partition of a finite set, while conditional entropy measures the degree of difficulty in predicting a decision partition when a condition partition is provided. Since two direct methods exist for defining conditional entropy based on its partition entropy, the inequality postulates of monotonicity, which conditional entropy satisfies,...
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