Mathematical Problems in Engineering

Volume 2015, Article ID 428218, 8 pages

http://dx.doi.org/10.1155/2015/428218

## Research of the Classification Model Based on Dominance Rough Set Approach for China Emergency Communication

College of Economic Management, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received 10 November 2014; Accepted 16 February 2015

Academic Editor: Julien Bruchon

Copyright © 2015 Fan Zifu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Ensuring smooth communication and recovering damaged communication system quickly and efficiently are the key to the entire emergency response, command, control, and rescue during the whole accident. The classification of emergency communication level is the premise of emergency communication guarantee. So, we use dominance rough set approach (DRSA) to construct the classification model for the judgment of emergency communication in this paper. In this model, we propose a classification index system of emergency communication using the method of expert interview firstly and then use DRSA to complete data sample, reduct attribute, and extract the preference decision rules of the emergency communication classification. Finally, the recognition accuracy of this model is verified; the testing result proves the model proposed in this paper is valid.

#### 1. Introduction

As an important foundation for the national economy industries, emergency communication is directly related to the smooth communication when the accident occurs, affecting the timely delivery of important information and the favoring progress of the emergency communication guarantee. However, the classification of emergency communication level is significant prerequisite for emergency communication guarantee [1]. However, the quantitative research on classification of emergency communication level is poor in China; the division of response levels in emergency communication support plan is based on the degree of influence on communication infrastructure caused by accident. So, the division of response levels in emergency communication support plan is not fit for the accident with different types and classes and need to build a set of new emergency communication classification criteria based on nature and extent of accident.

Currently, the popular classification methods contain the analytic hierarchy process (AHP), cluster analysis, dynamic fuzzy analysis method, and naive Bayes, decision tree, logistic regression analysis, neural networks, rough sets, and other classification methods based on data mining. Thereinto, rough set (RS) can deal with imprecise inconsistent and incomplete information effectively, and don’t rely on future knowledge during the learning process (such as probability distribution in Bayesian and the membership in fuzzy set), so it is more objective in the description and disposition of the problem with the uncertainty. Since proposed by Pawlak in 1982 [2], rough set developed quickly from many machine learning study theories and has been widely applied in machine learning, medical diagnostic, market decision making, information security, and many other fields in recent years. In order to process information systems with continuous attributes and dominance relations, Greco et al. [3–5] proposed the dominance rough set approach (DRSA). In this method, the indiscernibility relation is replaced by the dominance relation and generates the decision rules in the form of “if conditions, then decision” through upward and downward union of classes. On one hand, this method considers future knowledge (i.e., preference information) of decision makers; on the other hand, the rules in rough set are suitable for decision makes to execute the decision-making behavior. What is more, RS can only conduct the attributes without dominance relation, while DRSA allows dealing with any kind of information including the continues data with the dominance relation and the attributes without dominance relation [6]. The attributes of classification for emergency communication in this paper have the dominance relation like communication support number, communication block length, and so forth and also have the attributes without preference dominance such as accident objective factors and accident type. So, we choose the DRSA theory to complete data, discretize, reduct attribute and extract preference decision rules in China emergency communication classification model. The research results can provide emergency communication support by optimizing the existing emergency communication support plans and help government departments to determine the emergency communication level of accident scientifically.

#### 2. Methods

The rough set theory, firstly introduced by Pawlak in 1982, is a valuable mathematical tool for dealing with vagueness and uncertainty [7]. For a long time, the use of the rough set approach and other data mining techniques was restricted to classification problems where the preference order of the evaluations was not considered. This is due to the fact that this method cannot handle inconsistencies that occur as a result of the violation of the dominance principle [8]. In order to deal with this kind of inconsistency, it was necessary to make a number of methodological changes to the original rough set theory. Greco et al. [9] proposed an extension of the rough set theory based on the dominance principle that would permit it to deal with inconsistency. This method is mainly based on the substitution of the indiscernibility relation for a dominance relation in the rough approximation of decision classes. It is more general than the classic functional or relational model and is more understandable for users because of its natural syntax [8]. The basic concepts of DRSA are described in the following [10].

A data table is in the form of a 4-tuple information system , where is a finite set of objects (universe), is a finite set of attributes/criteria, is the domain of the attribute/criterion , , and is a total function such that for each , , called the information function. The set is usually divided into set of condition attributes and set of decision attributes.

Let be an outranking relation to with reference to criterion , such that means that “ is at least as good as with respect to criterion .” It is said that object -dominates object with respect to (denotation ), if for all , and , then the dominance relation is a partial preorder. Given and , let represent the -dominating set and the -dominated set with respect to , respectively.

Let , be a set of classes of such that each belongs to one and only one class . We assume that all , such that , and each element of is preferred to each element . In other words, if is a comprehensive outranking relation on , then it is supposed that , where means and not .

We can define unions of classes relative to a particular dominated or dominating class; these unions of classes are called upward and downward unions of classes and are defined, respectively, as

Supposing that represents both and , represents and . The -lower and -upper approximations of , with respect to , are refined as

The -boundaries (-doubtable region) of are defined as

The ratio
defines the quality of approximation of the classification CL by means of the criteria from set , or, briefly, the quality of classification. This ratio expresses the proportion of all -correctly classified objects—that is, all of the nonambiguous objects to all of the objects in the data table. Every minimal subset such that is called a reduction of with respect to Cl and is denoted by . Again, a data table may have more than one reduct. The intersection of all of the reductions is known as the core, denoted by CORE_{Cl}.

In dominance rough set, deterministic rules are derived from class set of the lower approximation rules. The following two types of decision rules can be considered:

Among them, indicate condition attributes with dominance relation. indicate condition attributes without dominance relation. represents the attribute value of .

#### 3. Classification Model of Emergency Communication

The construction of emergency communication classification model is mainly divided into two phases: index extraction and data mining based on dominance rough set approach; the detailed construction steps are described as follows.

##### 3.1. Construction of Classification Index System

Choosing which kind of indexes as a study variable has a great influence on the accuracy and reliability of the model. According to “General emergency plan national sudden public events of China,” “National Communications Security Emergency Plan of China” [11], and the literature [12–19] and experts’ advice, combined with the emergency communication characteristics of accident, emergency communication classification index system is divided into 20 indexes and four dimensions with emergencies objective factors, communication networks damaged, the emergency communication resources, and social and other factors, as shown in Table 1.