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Shannon entropy is the key concept in the literature of information theory and has found wide applications in different disciplines of science and technology. The various researchers have generalized this entropy measure with different approaches. The object of the present paper is to stress the importance of the property of concavity. Thus based mainly on the postulate of concavity of entropy function,...
To study the battlefield electromagnetic measurement data effectively, it is very necessary to research a valid attribute reduction method for dealing with the measurement information. Firstly, the deficiencies of some current information entropy models are analyzed, and a new kind of generalized information entropy model based on fuzzy-rough set is proposed, which can introduce the probability distribution...
Network security has become a major concern in recent years. In this research, we present an entropy-based network traffic profiling scheme for detecting security attacks. The proposed scheme consists of two stages. The purpose of the first stage is to systematically construct the probability distribution of relative uncertainty for normal network traffic behavior. In the second stage, we use the...
This paper improves a method of sample selection based on maximum entropy. Compared with the original method, the improved one takes the probability distribution of unlabeled instances into consideration. It selects the instances which can reduce the uncertainty of the whole unlabeled set to a great extent. The uncertainty reduction of the whole unlabeled set caused by an instance is measured by the...
Probabilistic graphical models are tools that are used to represent the probability distribution of a vector of random variables X = (X1, ..., XN). In this paper we introduce functions f(x1, ..., xN) defined over the given vector. These functions also are random variables. The main result of the paper is an algorithm for finding the expected value and other moments for some classes of f(x1, ..., x...
The C4ISR system effectiveness was respectively studied under the model of network centric warfare and platform centric warfare combining with specific operations in the paper, utilizing graph theory, information entropy, knowledge function theory and complexity theory. That how operational effectiveness is effected by information-sharing, network complexity, operation flow and other factors was studied,...
In active perception systems for scene recognition the utility of an observation is determined by the information gain in the probability distribution over the state space. The goal is to find a sequence of actions which maximizes the system knowledge at low resource costs. Most current approaches focus either on optimizing the determination of the payoff neglecting the costs or develop sophisticated...
To solve the problems of job shop scheduling under uncertain information, the paper builds a rough constrained model which overcomes the defects of traditional methods which need pre-set authorized characteristics or amount described attributes, and proposes a new maximum entropy estimation of distribution algorithm to solve these complex problems. The simulation tests of Muth and Thompson's benchmark...
Inductive learning for classification based on information theory is one of the important topics in data mining. We here propose an maximum contribution method for classification based on information theory. According to the theory of channel transmission in information theory, the definition contribution is developed based on probability distribution of classified space, probability transfer matrices...
One of the concepts used to measure risk and uncertainty is the variance or the standard-deviation in finance and insurance market. The simplicity of variance and standard-deviation remain a major attraction. But they have some limitations. In this paper, we present a new risk measure which combines entropy and variance under the incomplete information. The estimate of maximum entropy loss distribution...
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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