Data processing of small samples based on grey distance information approach

Data processing of small samples is an important and valuable research problem in the electronic equipment test. Because it is difficult and complex to determine the probability distribution of small samples, it is difficult to use the traditional probability theory to process the samples and assess the degree of uncertainty. Using the grey relational theory and the norm theory, the grey distance information approach, which is based on the grey distance information quantity of a sample and the average grey distance information quantity of the samples, is proposed in this article. The definitions of the grey distance information quantity of a sample and the average grey distance information quantity of the samples, with their characteristics and algorithms, are introduced. The correlative problems, including the algorithm of estimated value, the standard deviation, and the acceptance and rejection criteria of the samples and estimated results, are also proposed. Moreover, the information whitening ratio is introduced to select the weight algorithm and to compare the different samples. Several examples are given to demonstrate the application of the proposed approach. The examples show that the proposed approach, which has no demand for the probability distribution of small samples, is feasible and effective.

A Self-Organizing RBF Neural Network Based on Distance Concentration Immune Algorithm

Radial basis function neural network(RBFNN) is an effective algorithm in nonlinear system identification. How to properly adjust the structure and parameters of RBFNN is quite challenging. To solve this problem, a distance concentration immune algorithm(DCIA) is proposed to self-organize the structure and parameters of the RBFNN in this paper. First, the distance concentration algorithm, which increases the diversity of antibodies, is used to find the global optimal solution. Secondly,the information processing strength(IPS) algorithm is used to avoid the instability that is caused by the hidden layer with neurons split or deleted randomly. However, to improve the forecasting accuracy and reduce the computation time, a sample with the most frequent occurrence of maximum error is proposed to regulate the parameters of the new neuron. In addition, the convergence proof of a self-organizing RBF neural network based on distance concentration immune algorithm(DCIA-SORBFNN) is applied to guarantee the feasibility of algorithm. Finally, several nonlinear functions are used to validate the effectiveness of the algorithm. Experimental results show that the proposed DCIASORBFNN has achieved better nonlinear approximation ability than that of the art relevant competitors.

Risk Analysis of Information System Security Based on Distance of Information-State Transition

The configuration of information system security policy is directly related to the information asset risk, and the configuration required by the classified security protection is able to ensure the optimal and minimum policy in the corresponding security level. Through the random survey on the information assets of multiple departments, this paper proposes the relative deviation distance of security policy configuration as risk measure parameter based on the distance of information-state transition（DIT） theory. By quantitatively analyzing the information asset weight, deviation degree and DIT, we establish the evaluation model for information system. With example analysis, the results prove that this method conducts effective risk evaluation on the information system intuitively and reliably, avoids the threat caused by subjective measurement, and shows performance benefits compared with existing solutions. It is not only theoretically but also practically feasible to realize the scientific analysis of security risk for the information system.

New Information Distance Measure and Its Application in Question Answering System

In a question answering （QA） system, the fundamental problem is how to measure the distance between a question and an answer, hence ranking different answers. We demonstrate that such a distance can be precisely and mathematically defined. Not only such a definition is possible, it is actually provably better than any other feasible definitions. Not only such an ultimate definition is possible, but also it can be conveniently and fruitfully applied to construct a QA system. We have built such a system -- QUANTA. Extensive experiments are conducted to justify the new theory.

A New Approach for Multi-Document Update Summarization

Fast changing knowledge on the Internet can be acquired more efficiently with the help of automatic document summarization and updating techniques. This paper describes a novel approach for multi-document update summarization. The best summary is defined to be the one which has the minimum information distance to the entire document set. The best update summary has the minimum conditional information distance to a document cluster given that a prior document cluster has already been read. Experiments on the DUC/TAC 2007 to 2009 datasets （http：//duc.nist.gov/, http：//www.nist.gov/tac/） have proved that our method closely correlates with the human summaries and outperforms other programs such as LexRank in many categories under the ROUGE evaluation criterion.

A novel approximation of basic probability assignment based on rank-level fusion

Belief functions theory is an important tool in the field of information fusion. However, when the cardinality of the frame of discernment becomes large, the high computational cost of evidence combination will become the bottleneck of belief functions theory in real applications. The basic probability assignment （BPA） approximations, which can reduce the complexity of the BPAs, are always used to reduce the computational cost of evidence combination. In this paper, both the cardinalities and the mass assignment values of focal elements are used as the criteria of reduction. The two criteria are jointly used by using rank-level fusion. Some experiments and related analyses are provided to illustrate and justify the proposed new BPA approximation approach.

A New Multiword Expression Metric and Its Applications

Multiword Expressions （MWEs） appear frequently and ungrammatically in natural languages. Identifying MWEs in free texts is a very challenging problem. This paper proposes a knowledge-free, unsupervised, and languageindependent Multiword Expression Distance （MED）. The new metric is derived from an accepted physical principle, measures the distance from an n-gram to its semantics, and outperforms other state-of-the-art methods on MWEs in two applications： question answering and named entity extraction.