Multi-source Information System Research Articles

Fuzzy multi-granulation decision-theoretic rough sets based on fuzzy preference relation

Preference analysis is a class of important issues in multi-criteria decision making. The rough set theory is a powerful approach to handle preference analysis. In order to solve the multi-criteria preference analysis, this work improves the fuzzy multi-granulation decision-theoretic rough set model with additive consistent fuzzy preference relation, and it is used to analyze data from different sources, i.e., multi-source (fuzzy) information system. More specifically, we introduce the models of optimistic and pessimistic fuzzy preference relation multi-granulation decision-theoretic rough sets. Then, their principal structure, basic properties and several kinds of uncertainty measure methods are investigated as well. An example is employed to illustrate the effectiveness of the proposed models, and comparisons are also offered according to different measures of our models and existing models.

Heterogeneous multigranulation fuzzy rough set-based multiple attribute group decision making with heterogeneous preference information

Abstract This study proposes a heterogeneous attribute multigranulation fuzzy rough set approach to the problem of multiple attribute group decision-making (MAGDM) under uncertainty. We first present the definition of heterogeneous attribute multi-source information systems and then construct the heterogeneous multigranulation approximation space using the arbitrary binary relation classes generated by different categories attribute. We then give the rough approximation of a fuzzy decision-making object with respect to heterogeneous multigranulation approximation space, i.e., the heterogeneous attribute multigranulation fuzzy rough set model over multi-source information system. Meanwhile, we investigate some interesting properties and conclusions for the proposed new model and also discuss the interrelationship between the proposed heterogeneous attribute multigranulation fuzzy rough set model and the existing generalized rough set models. After that, we construct a new approach to MAGDM problems based on heterogeneous attribute multigranulation fuzzy rough set theory. The decision-making procedure and the methodology as well as the algorithm of the proposed method are given and a detailed comparison of the traditional methods to MAGDM problems illustrates the advantages and limitations. Finally, an example of handling MAGDM problem of evaluation of emergency plans for unconventional emergency events illustrates this approach.

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.

Information Fusion in a Multi-Source Incomplete Information System Based on Information Entropy

As we move into the information age, the amount of data in various fields has increased dramatically, and data sources have become increasingly widely distributed. The corresponding phenomenon of missing data is increasingly common, and it leads to the generation of incomplete multi-source information systems. In this context, this paper’s proposal aims to address the limitations of rough set theory. We study the method of multi-source fusion in incomplete multi-source systems. This paper presents a method for fusing incomplete multi-source systems based on information entropy; in particular, by comparison with another method, our fusion method is validated. Furthermore, extensive experiments are conducted on six UCI data sets to verify the performance of the proposed method. Additionally, the experimental results indicate that multi-source information fusion approaches significantly outperform other approaches to fusion.

A Multisource Information System for Monitoring and Improving Ship Energy Efficiency

ABSTRACT Fan, A.; Yan, X., and Yin, Q., 2016. A multisource information system for monitoring and improving ship energy efficiency. Global climate change has attracted an increasing amount of attention by the public. The shipping industry is being scrutinized with regard to improving efficiency and reducing emissions. This paper explores the monitoring technology and efficiency enhancement method for a ship. First, the internal and external factors that affect a ship's energy efficiency are determined and examined. Second, an inland river ship is selected as a research target on which a multisource information system is constructed via the selection and installation of the corresponding sensors on the ship. A series of system tests have also been performed when the ship is in operation. The field tests prove that the established system is adequate for the voyage and that all of the sensors can normally monitor the parameters. In addition, a massive quantity of data for the upper, middle, and lower reaches...

Information Fusion Based on Information Entropy in Fuzzy Multi-source Incomplete Information System

With the development of society, although the way that people get information more and more convenient, the information which people get may be incomplete and has a little degree of uncertainty and fuzziness. In real life, the incomplete fuzzy phenomenon of information source exists widely. It is extremely meaningful to fuse multiple fuzzy incomplete information sources effectively. In this study, a new method is presented for information fusion based on information entropy in fuzzy incomplete information system and the effectiveness of the new method is verified by comparing the average fusion method. Then, an illustrative example is delivered to illustrate the effectiveness of the proposed fusion method. Finally, we have also tested the veracity and validity of this method by experiment on a dataset from UCI. The results of this study will be useful for pooling the uncertain data from different information sources and significant for establishing a distinct direction of the fusion method.

A novel approach to information fusion in multi-source datasets: A granular computing viewpoint

The advent of Big Data has seen both the sources and volumes of data increase rapidly. A multi-source information system can be used to represent information drawn from multiple sources. However, some of these sources are of less importance than others, and some are essentially worthless. Selecting the most valuable sources and efficiently fusing information are therefore core issues in the field of data science. To investigate this matter, we first propose internal-confidence and external-confidence degrees to estimate the reliability of each information source within a multi-source information system. A source selection principle is then constructed, allowing worthy and reliable information sources to be chosen. Furthermore, a new information fusion method is constructed by transforming the original information of each object into a triangular fuzzy information granule, and some uncertainty measures of this fusion process are studied. Finally, to interpret and comprehend the proposed theories and algorithm, extensive experiments are performed on six datasets to verify that our approach can deal with practical issues. The results indicate that the proposed triangular fuzzy granule fusion approach is efficient and effective for information fusion in multi-source datasets.

A fuzzy multigranulation decision-theoretic approach to multi-source fuzzy information systems

Decision-theoretic rough set theory (DTRS) is becoming one of the important research directions for studying set approximations using Bayesian decision procedure and probability theory in rough set community. In this paper, a novel model, fuzzy multigranulation decision-theoretic rough set model (FM-DTRS), is proposed in terms of inclusion measure of fuzzy rough sets in the viewpoint of fuzzy multigranulation. Gaussian kernel is used to compute the similarity between objects, which induces a fuzzy equivalence relation, and then we make use of Tp-norm operator with the property of Hadamart product to aggregate the multiple induced fuzzy equivalence relations. We employ the aggregated relation to fuzzily partition the universe and then obtain multiple fuzzy granulations from the multi-source information system. Moreover, some of its properties are addressed. A comparative study between the proposed fuzzy multigranulation decision-theoretic rough set model and Qian’s multigranulation decision-theoretic rough set model is made. An example is employed to illustrate the effectiveness of the proposed method which may provide an effective approach for multi-source data analysis in real applications.

Composition of Constraint, Hypothesis and Error Models to improve interaction in Human–Machine Interfaces

Although there are many tasks where output strings are automatically generated from a set of evidence, they are not perfect and human intervention is often required to correct the result. In this paper we present a generic Symbol Input Interaction Method for Human–Machine Interfaces that combines multi-source information: an input Hypothesis Model, an Error Model, a Constraint Model and a user interaction scheme.We use Weighted Finite-State Transducers (WFSTs) to represent the different sources of information available: the initial hypotheses, the possible errors, the constraints imposed by the task (interaction language) and the user input. The fusion of these models to find the most probable output string can be performed efficiently by using carefully selected transducer operations. The proposed system initially suggests an output based on the set of hypotheses, possible errors and Constraint Models. Then, if human intervention is needed, a multimodal approach, where the user input is combined with the aforementioned models, is applied to produce, with a minimum user effort, the desired output. This approach offers the practical advantages of a de-coupled model (e.g. input-system + parameterized rules + post-processor), keeping at the same time the error-recovery power of an integrated approach, where all the steps of the process are performed in the same formal machine (as in a typical HMM in speech recognition) to avoid that an error at a given step remains unrecoverable in the subsequent steps. After a presentation of the theoretical basis of the proposed multi-source information system, its application to two real world problems, as an example of the possibilities of this architecture, is addressed. The experimental results obtained demonstrate that a significant user effort can be saved when using the proposed procedure. A simple demonstration, to better understand and evaluate the proposed system, is available on the web https://demos.iti.upv.es/hi/.

A Evaluation Method of System Performance Based on Cloud Theory

During the evaluation of multi-source information system, uncertainty exists widely. In order to use and process the uncertainty and improve the creditability of system evaluation, this paper based on the cloud theory, studied the mapping method between nature variable and quantity variable. At the same time, the paper promoted a novel evaluation method of system performance based on the cloud theory. Finally, an example validates the process and feasibility of the method. The method exploits new application field for the cloud theory.

Characterizing tree canopy loss using multi-source GIS data in Central Massachusetts, USA

Despite numerous ecosystem services provided by urban trees, they are continually threatened by combined natural disturbances, invasive species, development and negligent management practices. This research characterizes the amount and cause of tree loss in Worcester, Massachusetts, in the northeast United States, and neighbouring towns between 2008 and 2010 using multi-source remotely sensed imagery and historical land cover maps (1976–2009). Historical land-change analysis reveals that proportional forest cover loss in the Worcester County study area exceeds that of the state by 0.26% per year, 67% of which was driven by the expansion of low-density residential land use. Between 2008 and 2010, 2% of Worcester County’s tree canopy was lost to high- and low-density urban development (47% of the total loss), United States Department of Agriculture (USDA) tree removal for Asian longhorned beetle (ALB) eradication (25%), timber harvest (15%) and ice storm damage (6%). The use of multi-source geographic information system (GIS) data to characterize tree canopy loss makes it a flexible and replicable method to monitor urban tree health.

Multigranulation rough sets: From partition to covering

The classical multigranulation rough set (MGRS) theory offers a formal theoretical framework for solving the complex problem under multigranulation environment. However, it is noticeable that MGRS theory cannot be applied in multi-source information systems with a covering environment in the real world. To address this issue, we firstly present in this paper three types of covering based multigranulation rough sets, in which set approximations are defined by different covering approximation operators. Then, by using two different approximation strategies, i.e., seeking common reserving difference and seeking common rejecting difference, two kinds of covering based multigranulation rough set are presented, namely, a covering based optimistic multigranulation rough set and a covering based pessimistic multigranulation rough sets. Finally, we develop some properties and several uncertainty measures of the covering based multigranulation rough sets. These results will enrich the MGRS theory and enlarge its application scope.

Automated Selection of Relevant Information for Notification of Incident Cancer Cases within a Multisource Cancer Registry

The aim of this study was to develop and evaluate a selection algorithm of relevant records for the notification of incident cases of cancer on the basis of the individual data available in a multi-source information system. This work was conducted on data for the year 2008 in the general cancer registry of Poitou-Charentes region (France). The selection algorithm hierarchizes information according to its level of relevance for tumoral topography and tumoral morphology independently. The selected data are combined to form composite records. These records are then grouped in respect with the notification rules of the International Agency for Research on Cancer for multiple primary cancers. The evaluation, based on recall, precision and F-measure confronted cases validated manually by the registry's physicians with tumours notified with and without records selection. The analysis involved 12,346 tumours validated among 11,971 individuals. The data used were hospital discharge data (104,474 records), pathology data (21,851 records), healthcare insurance data (7508 records) and cancer care centre's data (686 records). The selection algorithm permitted performances improvement for notification of tumour topography (F-measure 0.926 with vs. 0.857 without selection) and tumour morphology (F-measure 0.805 with vs. 0.750 without selection). These results show that selection of information according to its origin is efficient in reducing noise generated by imprecise coding. Further research is needed for solving the semantic problems relating to the integration of heterogeneous data and the use of non-structured information.

GIS Platform‐based Multi‐source Information System for Evaluation of Cu, W and Au Resources in the Northern Qilian, China

Abstract Based on the information of geology, geochemistry, geophysics and remote sensing, the GIS of multi‐source information is used to evaluate Cu, W and Au mineral resources in Northern Qilian, China. As the GIS evaluation system works out in the thinking of geological prospecting, its functions include file management, graph edition, database maintenance, information inquiry and comprehensive spatial analysis as well as prospecting target prognosis. Accordingly, the GIS evaluation system can be used directly and conveniently for inquiry and analysis of visual graphs or images.

SIMS@REIN : un système d’information multi-sources pour l’insuffisance rénale terminale

In France, the prevalence of End-Stage Renal Disease (ESRD) is not precisely known. The sources of information are scattered and not coordinated. Consequently, care is ill adapted to meet the demand. The Multi-Source Information System is the basis of the Renal Epidemiology and Information Network (REIN). It is dedicated to improve and organise our medical and epidemiological knowledge of ESRD and to aid public health decision-making in this area. The proposed approach is based on the datawarehouses. This model allows a unified vision of scattered data into distinct databases, for a better management, be it particular (patient follow-up) or global (regional follow-up), with a finality of aid in decision-making. Several categories of problems were considered: the global conception of the information system, the organisation of the datawarehouse, which offers different viewpoints of the data, the integration of heterogeneous data coming from different sources, data exchange and definition of a specific ontology.