Covering Information Systems Research Articles

A new mechanism of rule acquisition based on covering rough sets.

Rule acquisition, known as knowledge acquisition, is an important and topical issue in granular computing theory. Granules are not only composed of objects but also have feature values. However, In the granule associativity rules, the traditional rule extraction methods fail to consider the influence of granules on the decision, thus the method is not well adapted in reality. On the other hand, the existing methods lack a rule-based measure for information systems. In this paper, the action parameters are first introduced to establish a more realistic granule associativity rule in covering information system. Further, we present the rule-based data potential to address the measurement problem. In addition, rule-based scale selection in multi-scale covering rough sets is explored, followed by a scale combination integrating generalization capability, data potential, and lower approximation. Finally, algorithm is designed and experiments are conducted.

Grained matrix and complementary matrix: Novel methods for computing information descriptions in covering approximation spaces

As two important tools of information descriptions, minimal description (MinD) and maximal description (MaxD) are used to eliminate redundant information when describing an object in covering-based rough sets (CbRSs). Hence, there are many key issues (such as reductions, neighborhoods and approximations) have to do with them in CbRSs. However, it is complicated and time-consuming to use set approaches to compute them in a large cardinal covering approximation space. So the matrix approaches have been used to calculate them by which computers can implement the corresponding calculations easily. Based on the previous research work and inspired by the demand of knowledge discovery under large scale covering information systems, we propose two new matrix methods to calculate MinD and MaxD in CbRSs in this paper. Firstly, a note on the existing methods (matrix approach-1 and approach-2) is presented. Secondly, a new matrix, called grained matrix, is presented. After combining grained matrices and the mean of “sum”, the new matrix approach-1 is presented to calculate MinD and MaxD. Thirdly, three new matrix operations and the notion of complementary matrix are given which can simplify the calculation of computers. Based on new operations, we present the new matrix approach-2 to compute MinD and MaxD. Finally, the computational efficiency of the presented approaches are shown and compared with other methods by experiments on several UCI data sets.

National high-resolution land cover and land use information system

ABSTRACT In the last few years, the Spanish Land Cover and Land Use Information System (SIOSE) is undergoing a methodological and productive evolution focused on achieving a high level of geometric, thematic, and temporal detail. This methodological advance is based on the reuse and integration of official reference information on land cover and land use, such as cadastre (1:500–1:5000), agricultural identification systems (1:5000), forest maps (1:25,000), or LiDAR data (0.5 p/m2). This article presents the work carried out by the Instituto Geográfico Nacional (Spanish National Geographic Institute), in coordination with the rest of the project partners, in the generation of a high-resolution SIOSE by automatic data integration and visual review. To carry out the latter, an online framework has been designed to facilitate the access to data and to guide reviewers in editing. Resulting integrated data offers a detailed description for all thematic domains on land cover and land use never seen before in Spain at the National level and advances in the land management on many application fields. High-resolution SIOSE provides information at 1:5000 for the majority of classes and ensures a complete thematic content with more than 140 classes structured in an object-oriented information system.

Some New Covering-Based Multigranulation Fuzzy Rough Sets and Corresponding Application in Multicriteria Decision Making

Multigranulation rough set theory is an important tool to deal with the problem of multicriteria information system. The notion of fuzzy β -neighborhood has been used to construct some covering-based multigranulation fuzzy rough set (CMFRS) models through multigranulation fuzzy measure. But the β -neighborhood has not been used in these models, which can be seen as the bridge of fuzzy covering-based rough sets and covering-based rough sets. In this paper, the new concept of multigranulation fuzzy neighborhood measure and some types of covering-based multigranulation fuzzy rough set (CMFRS) models based on it are proposed. They can be seen as the further combination of fuzzy sets: covering-based rough sets and multigranulation rough sets. Moreover, they are used to solve the problem of multicriteria decision making. Firstly, the definition of multigranulation fuzzy neighborhood measure is given based on the concept of β -neighborhood. Moreover, four types of CMFRS models are constructed, as well as their characteristics and relationships. Then, novel matrix representations of them are investigated, which can satisfy the need of knowledge discovery from large-scale covering information systems. The matrix representations can be more easily implemented than set representations by computers. Finally, we apply them to manage the problem of multicriteria group decision making (MCGDM) and compare them with other methods.

Methodological Proposal for Automated Detection of the Wildland–Urban Interface: Application to the Metropolitan Regions of Madrid and Barcelona

Official information on Land Use Land Cover is essential for mapping wildland–urban interface (WUI) zones. However, these resources do not always provide the geometrical or thematic accuracy required to delimit buildings that are easily exposed to risk of wildfire at the appropriate scale. This research shows that the integration of active remote sensing and official Land Use Land Cover (LULC) databases, such as the Spanish Land Use Land Cover information system (SIOSE), creates the synergy capable of achieving this. An automated method was developed to detect WUI zones by the massive geoprocessing of data from official and open repositories of the Spanish national plan for territory observation (PNOT) of the Spanish national geographic institute (IGN), and it was tested in the most important metropolitan zones in Spain: Barcelona and Madrid. The processing of trillions of LiDAR data and their integration with thousands of SIOSE polygons were managed in a Linux environment, with libraries for geographic processing and a PostgreSQL database server. All this allowed the buildings that are exposed to wildfire risk with a high level of accuracy to be obtained with a methodology that can be applied anywhere in the Spanish territory.

Numerical Characterizations of Topological Reductions of Covering Information Systems in Evidence Theory

The reductions of covering information systems in terms of covering approximation operators are one of the most important applications of covering rough set theory. Based on the connections between the theory of topology and the covering rough set theory, two kinds of topological reductions of covering information systems are discussed in this paper, which are characterized by the belief and plausibility functions from the evidence theory. The topological spaces by two pairs of covering approximation operators in covering information systems are pseudo-discrete, which deduce partitions. Then, using plausibility function values of the sets in the partitions, the definitions of significance and relative significance of coverings are presented. Hence, topological reduction algorithms based on the evidence theory are proposed in covering information systems, and an example is adopted to illustrate the validity of the algorithms.

Novel matrix-based approaches to computing minimal and maximal descriptions in covering-based rough sets

Minimal and maximal descriptions of concepts are two important notions in covering-based rough sets. Many issues in covering-based rough sets (e.g., reducts, approximations, etc.) are related to them. It is well known that, it is time-consuming and error-prone when set representations are used to compute minimal and maximal descriptions in a large scale covering approximation space. To address this problem, matrix-based methods have been proposed in which calculations can be conveniently implemented by computers. In this paper, motivated by the need for knowledge discovery from large scale covering information systems and inspired by the previous research work, we present two novel matrix-based approaches to compute minimal and maximal descriptions in covering-based rough sets, which can reduce the computational complexity of traditional methods. First, by introducing the operation “sum” into the calculation of matrix instead of the operation “⊕”, we propose a new matrix-based approach, called approach-1, to compute minimal and maximal descriptions, which does not need to compare the elements in two matrices. Second, by using the binary relation of inclusion between elements in a covering, we propose another approach to compute minimal and maximal descriptions. Finally, we present experimental comparisons showing the computational efficiency of the proposed approaches on six UCI datasets. Experimental results show that the proposed approaches are promising and comparable with other tested methods.

A Minimal Family of Sub-bases

This paper investigates a minimal family of sub-bases. First, the concept of a minimal family of sub-bases is presented and its properties are studied. Then the relationship between reducts in covering information systems and minimal families of sub-bases is discussed. Based on Boolean matrices, an approach is provided to derive a minimal family of sub-bases. Finally, experiments are conducted to illustrate the effectiveness of the proposed approach.

Information Entropy and Optimal Scale Combination in Multi-Scale Covering Decision Systems

In the traditional covering information systems, each object has only one scale, that is, the single-scale covering information system. But in realistic applications, one often needs to process and analyze data in different scales. To solve this problem, a new multi-scale covering information system is first introduced in this paper, and the optimal scale combination selections based on information entropy in multi-scale coveing decision systems are discussed. Firstly, entropy consistent scale combination and entropy optimal scale combination are defined in multi-scale covering information system. Secondly, entropy optimal scale combination is defined in multi-scale covering decision system, which is proved to be equivalent to the optimal scale combination in consistent multi-scale coveing decision system. Moreover, lower approximation entropy optimal scale combination is defined in the inconsistent multi-scale covering decision system. Finally, the corresponding selection algorithms and specific examples are given by using conditional entropy and limitary conditional entropy, respectively.

Remote Sensing and Gis Based Land Use and Land Cover Information of Medchal Mandalof Medchal District

It is exceptionally significant to use GIS and remote sensing application for proficient need in daily life. Upcoming and contemporary technologies like data processing, earth observation geodata processing and investigation are necessary for the researcher for the development of the society on a large scale. Remote sensing information data both in digital format and image format is utilized for retrieving the information about land resources by using (DIP) digital Interpretation Techniques and (VIP) Visual interpretation techniques Techniques. The foremost objective of the given study area is to Setup land use and land cover information system to evaluate land resources by by means of GIS Remote Sensing at Arc GIS10.2.1 platform of MedchalMandal. GIS and Remote Sensing information is the ultimate solution for the coverage of large area. Different types of layers are created from Remote Sensing images data and ArcGIS 10.2.1 Software. In the present study analysis is carried out by primary information which was generated from remote sensing data. GIS is Decision support system which helps planners and Decision makers to take correct decision for sustainable development, it also helps developers, engineers in environmental study, town planning and resource management.

Three-Way Group Decision in a Covering Decision Information System

Three-way decision (3WD) provides a new perspective for solving practical decision-making problems, which is in line with human's cognitive pattern. A covering information system (CIS) is an information system (IS) that consists of multiple coverings in the universe. A CIS with decision attributes which is seen as a covering decision information system (CDIS). This paper proposes three-way group decisions in a CDIS, as well as gives its application on the problem of position competition. First of all, the neighbourhood of every point in a CDIS is defined, and corresponding similarity class of this point is also obtained. Then, because of the uncertainty of risks, loss functions are acquired through group decision-making by means of interval numbers. Next, a method of three-way group decisions in a CDIS is presented. Eventually, the position competition is presented as an example to support our proposed decision-making method.

La Dehesa en Extremadura: caracterización y dinámicas a partir del Sistema de Información de Ocupación del Suelo de España (SIOSE) y comparativa con SIGPAC

Las dehesas constituyen uno de los aprovechamientos agrosilvopastoriles de mayor extensión en la Comunidad Autónoma de Extremadura, con una superficie que ronda los 1,2 millones de hectáreas. Dada la importancia en torno a su gestión y conservación, el presente artículo tiene como objetivo determinar su superficie, analizar sus elementos y composición, así como cuantificar los cambios que ha experimentado. A partir de la consulta y análisis del Sistema de Información de Ocupación del Suelo de España, se han extraído los datos referentes a la cuantificación y localización de las superficies de dehesa para cada fecha de referencia del proyecto: 2005-2011-2014. Asimismo, estos datos se han comparado con la base de datos SIGPAC en relación a la superficie definida por ambos conjuntos de datos para la fecha de referencia 2014. Mediante la aplicación de la Tasa de Cambio Anual, se han estimado los valores de pérdidas y/o ganancias entre cada uno de estos años. Gracias a ello se ha determinado que, si bien la dehesa es el uso y cubierta del suelo dominante en la región, existe una leve pérdida de superficie de 7.000 hectáreas en el periodo de estudio.

Relative Reduction of Neighborhood-Covering Pessimistic Multigranulation Rough Set Based on Evidence Theory

Relative reduction of multiple neighborhood-covering with multigranulation rough set has been one of the hot research topics in knowledge reduction theory. In this paper, we explore the relative reduction of covering information system by combining the neighborhood-covering pessimistic multigranulation rough set with evidence theory. First, the lower and upper approximations of multigranulation rough set in neighborhood-covering information systems are introduced based on the concept of neighborhood of objects. Second, the belief and plausibility functions from evidence theory are employed to characterize the approximations of neighborhood-covering multigranulation rough set. Then the relative reduction of neighborhood-covering information system is investigated by using the belief and plausibility functions. Finally, an algorithm for computing a relative reduction of neighborhood-covering pessimistic multigranulation rough set is proposed according to the significance of coverings defined by the belief function, and its validity is examined by a practical example.

Matrix approaches for some issues about minimal and maximal descriptions in covering-based rough sets

In covering-based rough sets, many basic issues are related to minimal and maximal descriptions. For a covering approximation space with a large cardinal, it would be tedious and complicated to use set representations to solve the issues about minimal and maximal descriptions. Therefore it is necessary to study matrix representations by which calculations will become algorithmic and can be easily implemented by computers. In this paper, we use matrix approaches to investigate some issues about minimal and maximal descriptions in covering-based rough sets. Firstly, several new matrices and matrix operations are defined by which one can compute the minimal description and the approximation operator about the minimal description. Inspired by this result, a matrix method is presented to judge whether a covering is unary. Secondly, the maximal description and the approximation operator about the maximal description are also computed by these new matrices and matrix operations. Finally, based on the above results, we propose a matrix method to efficiently compute reductions of coverings. Moreover, we propose two types of reducts of covering information systems via minimal and maximal descriptions respectively, and we use matrix approaches to compute them. In a word, these results show an interesting view to investigate the combination between matrices and covering-based rough sets.

Information structures in a covering information system

A covering information system as the generalization of an information system is an important model in the field of artificial intelligence. This paper explores information structures in a covering information system, and this kind of structure is viewed as a granular structure from the granular computing viewpoint. The concept of information structures in a covering information system is first described by means of set vectors. Then, relationships between information structures in a covering information system are studied from the two aspects of dependence and separation. Next, properties of information structures in a covering information system are given. Furthermore, invariant characterizations of covering information systems under homomorphisms are presented. Finally, as an application for information structures in a covering information system, the granularity measure of uncertainty for covering information systems is investigated. These results will be very helpful for establishing a framework for granular computing in information systems.

Uncertainty measurement for a covering information system

A covering information system as the generalization of an information system is an important model in the field of artificial intelligence. Uncertainty measurement is a critical evaluating tool. This paper investigates uncertainty measurement for a covering information system. The concept of information structures in a covering information system is first described by using set vectors. Then, dependence between information structures in a covering information system is introduced. Next, the axiom definition of granularity measure of uncertainty for covering information systems is proposed by means of its information structures, and based on this axiom definition, information granulation and rough entropy in a covering information system are proposed. Moreover, information entropy and information amount in a covering information system are also considered. Finally, we conduct a numerical experiment on the congressional voting records data set that comes from UCI Repository of machine learning databases, and based on this numerical experiment, effectiveness analysis from the angle of statistics is given to evaluate the performance of uncertainty measurement for a covering information system. These results will be helpful for understanding the essence of uncertainty in a covering information system.

Related families-based attribute reduction of dynamic covering decision information systems

Many efforts have focused on studying techniques for selecting most informative features from data sets. Especially, the related family-based approaches have been provided for attribute reduction of covering information systems. However, the existing related family-based methods have to recompute reducts for dynamic covering decision information systems. In this paper, firstly, we investigate the mechanisms of updating the related families and attribute reducts by the utilization of previously learned results in dynamic covering decision information systems with variations of attributes. Then, we design incremental algorithms for attribute reduction of dynamic covering decision information systems in terms of attribute arriving and leaving using the related families and employ examples to demonstrate that how to update attribute reducts with the proposed algorithms. Finally, experimental comparisons with the non-incremental algorithms on UCI data sets illustrate that the proposed incremental algorithms are feasible and efficient to conduct attribute reduction of dynamic covering decision information systems with immigration and emigration of attributes.

Information fusion and numerical characterization of a multi-source information system

The existing research of multi-source information system, pessimistic or optimistic multi-granulation fusion functions, provided by multi-granulation rough set (MGRS) theory, which apply disjunctive and conjunctive operators of multiple binary relations to aggregate multiple granular structures induced by different binary relations, are too relax or too restrictive to solve the practical problems. In this paper, we employ evidence theory, probability theory and information entropy to address the information fusion and numerical characterization of uncertain data in a multi-source information system (MSIS). First, we propose novel definitions of multi-source rough approximations and corresponding multi-granulation rough approximations, probability distribution and basic probability assignment, which can be used to construct the connection between rough approximations and evidence theory. Second, the above ideas are extended to multi-source covering information system (MCIS). Finally, Shannon’s fusion algorithm based on equivalence relations or coverings, involved in the significance degree of condition attributes set with respect to a sample, conditional probability and information entropy, is presented to calculate the uncertainty degree of a decision, respectively. Then, based on the defined conditional probability in this paper, we design a multi-granulation variable precision rough set and consider the relationship with MGRS. And, the illustrative examples are given to elaborate the operation mechanism of the above conclusions. This study will be helpful for integrating the uncertain information come from multiple sources and eventful for creating a route of granular computing.

Homomorphisms between the covering information systems

The system of information is an important mathematical model in many fields, such as data mining, artificial intelligence, and machine learning. The relation or mapping is a popular method for exploring the communication between two systems of information. In this paper, we first introduce the concepts of the covering relation or mapping and the inverse covering relation or mapping between two covering systems of information and investigate their properties. Then, we propose the view of homomorphism of covering systems of information which are based on covering relation. Moreover, we prove that attribute reductions in the image system and original system are equivalent to each other under the conditions of homomorphism given in this paper.

Homomorphisms between the covering information systems

The system of information is an important mathematical model in many fields, such as data mining, artificial intelligence, and machine learning. The relation or mapping is a popular method for exploring the communication between two systems of information. In this paper, we first introduce the concepts of the covering relation or mapping and the inverse covering relation or mapping between two covering systems of information and investigate their properties. Then, we propose the view of homomorphism of covering systems of information which are based on covering relation. Moreover, we prove that attribute reductions in the image system and original system are equivalent to each other under the conditions of homomorphism given in this paper.