Three-way neighborhood based stream computing for incomplete hybrid information system

A regret theory-based multi-granularity three-way decision model with incomplete T-spherical fuzzy information and its application in forest fire management

Updating three-way decisions in incomplete multi-scale information systems

The three-way decision theory provides a three-way philosophical thinking to solve problems, and the regret theory quantifies the risk preferences of decision makers under different psychological behaviors. On the one hand, the combination of these two theories makes models more practical by considering the psychological behaviors of decision makers. On the other hand, we can effectively combine the advantages of the three-way decision theory with the regret theory to highlight the interpretability of decision-making processes. In this article, we propose a novel approximate estimation method for incomplete utility values via the regret theory and establish a wide sense of a three-way decision model on incomplete multiscale decision information systems. First, the degree of consistency for each scale is measured via using the dependence degree, then the optimal subsystem is selected by evaluating the scale selection cost. Furthermore, the incomplete multiscale evaluation information is transformed into triangular fuzzy numbers via linguistic term sets. Second, in light of fuzzy evaluation values and tradeoff factors, an estimation method for incomplete fuzzy subsystems is constructed, which can be used to calculate the utility difference and regret-rejoicing values for pairwise comparisons. Finally, from the perspective of human cognition, the tripartition and the corresponding decision rules are built by the tolerance degree, and the ranking of objects is calculated by the relative closeness degree. Additionally, multiaspect comparative and experimental analyses are performed by extensive experiments, and the feasibility, validity, and stability of the constructed model are shown by parametric analyses.

A three-way decision approach with a probability dominance relation based on prospect theory for incomplete information systems

The traditional rough set (RS) theory is a powerful tool to deal with complete information system, and its performance to process incomplete information system is weak, especially, its effect of combining the incomplete information system with fuzzy objective information system is weaker. The paper improves the tolerance relation proposed by M.Kryszkiewcz to obtain the γ− tolerance relation and γ− tolerance classes, presents the concept of the incomplete and fuzzy objective information system (IFOIS in short), and gives its rough set model based on the γ− tolerance relation, i.e., the rough set model in incomplete and fuzzy objective information system. Finally, the concept of precision reduction is defined, and the corresponding algorithm is provided.

During the recent years the smart cities knows a great extension as a modern shape of sustainable expansion. It's a urban area that utilize various devices connected with internet and integrates them with ICTs to promote goodness and execution of services for the best interaction among citizens and city's government. The basic for smart cities is distributed and independent information infrastructure. Using information effectively is going to be a main factor for success in the smart cities. The sources of information's (models, experts, and sensors) must be reason, perfect and complete. The generated information from independent and distributed sources can be imprecise, uncertain, and/or incomplete in real life. Any deficiency in gathered information will have a negative effect on the performance of services and decision making process within smart cities. So, we need a general framework to represent all types of imperfect and incomplete information. Since the classical methods fails to deal with vague, inconsistent and incomplete information, the fuzzy set was introduced to solve this drawback. The fuzzy set was not the perfect method for dealing with these drawbacks because it considers only truthiness and fails to deal with indeterminacy. The efficient mathematical tool for dealing with uncertain, vague and inconsistent objects is rough sets theory which introduced by Pawlak. The theory of neutrosophic rough sets is powerful for dealing with incompleteness and neutrosophic set deals with indeterminate and inconsistent data efficiently through considering truthiness, indeterminacy and falsity degrees. So, in this research we will propose a general framework for dealing with imperfect and incomplete information through using single valued neutrosophic and rough set theories. The combination of two sets will deal with all aspects of vagueness, inconsistency and incompleteness of data and information, and then will enhance the quality of introduced services and decisions from smart cities to their citizens. As experimentation, we applied the proposed framework for modeling imperfect and incomplete data in healthcare field.

Abstract: Machine learning can extract desired knowledge from training examples and ease the development bottleneck in building expert systems. Most learning approaches derive rules from complete and incomplete data sets. If attribute values are known as possibility distributions on the domain of the attributes, the system is called an incomplete fuzzy information system. Learning from incomplete fuzzy data sets is usually more difficult than learning from complete data sets and incomplete data sets. In this paper, we deal with the problem of producing a set of certain and possible rules from incomplete fuzzy data sets based on rough sets. The notions of lower and upper generalized fuzzy rough approximations are introduced. By using the fuzzy rough upper approximation operator, we transform each fuzzy subset of the domain of every attribute in an incomplete fuzzy information system into a fuzzy subset of the universe, from which fuzzy similarity neighbourhoods of objects in the system are derived. The fuzzy lower and upper approximations for any subset of the universe are then calculated and the knowledge hidden in the information system is unravelled and expressed in the form of decision rules.

The theory of three-way decision provides an effective tool for decision-making under uncertainty and incomplete information when a two-way decision is difficult to make. In this paper, we try to transform an intuitionistic fuzzy set into a pessimistic shadowed set or an optimistic shadowed set by using two intuitionistic fuzzy parameters and the classical shadowed set. Accordingly, the pessimistic three-way decision or the optimistic three-way decision of intuitionistic fuzzy set are investigated by means of the proposed shadowed sets. In addition, a method to calculate the thresholds by the minimum cost principle is proposed and some examples are given for illustrating the validity of the method.

Due to rampant existence of missing values in E-commerce information systems, the research on knowledge acquisition in incomplete E-commerce information systems becomes necessary. In this paper, a novel method of rough reduction is proposed to obtain the latent knowledge in incomplete E-commerce information systems. By constructing the discernibility matrix, the useful knowledge can finally be extracted from incomplete E-commerce information systems.

Big data is technology that has the ability to manage very large amounts of data, in very fast time to allow real-time analysis and reactions. Several clustering methods which are used to group data are Fuzzy C-Means (FCM) and K-Means Clustering. K-Means Clustering algorithm is a method of partitioning existing data into two or more group. This research goal was to compare the performance of K-Means and Fuzzy C-Means algorithms in clustering data using big data technology. In this research, Hadoop and Hive were chosen the big data technology. The knowledge of Shia history on student and lecturer of Syarif Hidayatullah State Islamic University Jakarta were the data which used in this research, The testing was done by constructing application K-Means Fuzzy C-Means using Java language, Hadoop and Hive and then test the performance of K-Means and Fuzzy C-Means algorithms in data clustering. It compares both algorithms in terms of accuracy, execution time, and time complexity of the algorithms. In the application K-Means Fuzzy C-Means, evaluation were performed with data filter and the average accuracy difference result of K-Means and Fuzzy C-Means is 8.03% with the better accuracy owned by K-Means. The average execution time difference is 718.58 ms, which K-Means was faster than is Fuzzy C-Means. The time complexities of both algorithms have the same value O(n2) and the Big O equation resulted in an average difference of 93,568 with the smallest value on K-Means. Thus, K-Means algorithm is better than the Fuzzy C-Means in terms of accuracy, execution time, and the time complexity.

Generally, two ways are used to handle null value in incomplete information systems. One is transforming an incomplete system to a complete system. The other is process the incomplete information table based on toleration relation. In this paper we propose a method to process the incomplete information table based on granular computing directly. The incomplete information table is divided into granules only use the known value of instances. The rules are generated based on the granules.

With the view point of granular computing, there are different granules at different levels of scale in data sets having hierarchical scale structures. In this paper, the notion of incomplete multi-scale information systems is first introduced. Using tolerance relation, the lower and upper approximations in incomplete multi-scale information systems are then defined and their properties are examined.

An information system as a database that shows relationships between objects and attributes is a crucial mathematical model in the field of artificial intelligence. A real-valued information system is an information system where information function values of each attribute are real numbers. This paper explores information structures in an incomplete real-valued information system. Distances between two objects in a given subsystem of an incomplete real-valued information system is first constructed. Then, the fuzzy T cos -equivalence relation, induced by this subsystem by using Gaussian kernel method, is obtained, where Gaussian kernel is based on this distance. Next, information structure of this subsystem is proposed. Moreover, relationships between two information structures are studied from the two aspects of dependence and separation. Finally, the dependence between two information structures is studied by using inclusion degree. These results will be helpful for establishing a framework of granular computing.

Further Investigation of Characteristic Relation in Incomplete Information System

The goal of outlier detection is to pinpoint data points that exhibit notable deviations from the rest of the observed values. It has found successful application in numerous fields, including process inspection, anti-terrorist operations, and public security. However, the majority of existing outlier algorithms rely on methods involving filling or deleting missing data, with few directly addressing incomplete data. This article studies outlier detection for incomplete real-valued data based on class-consistent technology, rough set theory, and granular computing. First, a tolerance relation founded on class-consistent technology is presented to illustrate the similarity among information values within an incomplete real-valued information system (IRVIS). Then, the tolerance classes are established based on the tolerance relation and utilized for computing approximate accuracy and other metrics. Next, an outlier factor is defined, considering both the degree of outlierness and the weight function assigned to each object within an IRVIS, elucidating the uncertainty and degree of outlier. Finally, an outlier detection algorithm (ODIRG) for an IRVIS based on class-consistent technology, rough set theory, and granular computing is devised. Numerical experiments on seven UCI datasets are undertaken to evaluate the stability of the ODIRG algorithm. The proposed method is demonstrated to exhibit strong effectiveness and adaptability for categorical data when compared with five other algorithms. It is notable that for comprehensive comparison, precision, recall, F F 1-measure, and receiver operating characteristic curve are employed to delineate the benefits of the proposed approach.