Measures of uncertainty based on Gaussian kernel for a fully fuzzy information system

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Abstract

The uncertainty of information plays an important role in practical applications, so how to capture the uncertainty of information systems becomes more and more popular. Uncertainty measures can supply new viewpoints for processing information systems, and they can help us in disclosing the substantive characteristics of information. Fuzzy information systems are important research objects in artificial intelligence. As a special kind of fuzzy information system, fully fuzzy information system (FFIS) is worth studying. This article is devoted to search indicators for measuring uncertainty in a FFIS according to fuzzy information structures in view of Gaussian kernel, and the fuzzy information structures can be viewed as granular structures under granular computing. Firstly, by employing Gaussian kernel for calculating similarities among objects in a FFIS, the fuzzy Tcos-similarity relation is obtained. Then, based on this relation, fuzzy information structures in a FFIS are introduced. Next, according to the information structures, granulation measure of a given FFIS is advanced. Moreover, entropy measure is also considered for a given FFIS. Finally, two numerical experiments are conducted to interpret the realistic significance and potential applications for measuring uncertainty in a FFIS. Theoretical research, numerical experiments and validity analysis make clear that the proposed measures are efficacious and applicable for a FFIS.

Introduction

Uncertainty, including vagueness, incompleteness, inconsistency, fuzziness and randomness, is a common sight in most places of the world. In machine learning, how to evaluate uncertainty information (i.e., how to search measure indicators) is an important issue [1], [2]. When applying measures of uncertainty for evaluating an information system, good indicators can enhance the accuracies and efficiencies of clustering and classification tasks [3], [4].

Rough set theory is an effective method of soft computing for characterizing uncertainty [5], [6], [7], [8]. As the expression of information (or knowledge), information system [5] is the research object of rough set theory. There are many studies of rough set theory which are related to information systems, such as decision analysis, uncertainty modeling, attribute reduction, machine learning, pattern recognition and reasoning with uncertainty [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21].

In an information system, there are two major directions for measuring uncertainty: information granulation, information entropy. They give indicators for studying uncertainty from different perspectives. The uncertainty of an information system decreases when information granulation increases or information entropy decreases.

Granulation is a typical physical indicator for measuring granule size. As a serviceable measure, Zadeh gave the concept of granulation [22]. Uncertainty measures for information systems can be defined as information granulation or knowledge granulation. Granulation is actually the average measure of the degree of information or knowledge refinement in information systems. In information systems, its distinction ability can be depicted through information granulation, i.e., the distinction ability of an information system becomes stronger as information granulation gets smaller. Some explorations and many outstanding contributions have been made by some researchers in this respect [23], [24], [25], [26], [27], [28], [29]. Granularity measure is advanced by Yao et al. [30] in the orientation of granulation. In set-valued information systems, Dai et al. [31] studied four types of measures from perspectives of entropy and granularity. Moreover, in interval and set-valued information systems, Wang et al. [32] discussed granularity measures. In neighborhood systems, Chen et al. [33] investigated granule structures, distances and measures. In view of neighborhood rough sets, Li et al. [34] advanced the neighborhood granulation measures for information systems. Based on partition-based granular structures, Yao [35] studied granularity measures and complexity measures.

Entropy, expanded by Shannon [36], is quite a practical index. It can represent content of information or knowledge in multifarious types, and has been extensively employed in various fields, especially in the aspect of measuring the uncertainty. Dai et al. [37] studied measurement in interval-valued decision systems, which is on account of extended conditional entropy. A measurement—θ-rough degree, that can be utilized to reveal uncertainty, is given by Dai et al. [38] in interval-valued information systems. In incomplete decision systems, Dai et al. [39] raised a new definition for conditional entropy, and got some significant properties and tried to apply it. Liu et al. [40] discussed several measures with parameter θ from the points of granulation and entropy in distributed FFISs. In order to research uncertainty of probabilistic hesitant fuzzy information, Su et al. [41] put forward two types of entropy measures. To handle the noise and uncertainty, Sun et al. [42] studied some neighborhood entropies for measuring uncertainty in neighborhood decision systems.

Many information systems, in which information values under attributes are fuzzy, exist in reality [43], [44], [45], so more researches are needed for fuzzy information systems. In an information system, if all information values under each attribute are fuzzy, then this system is said to be a FFIS [40], [46], [47]. Zhang et al. [48] studied information structures and uncertainty measures on account of equivalence relations for FFISs. In view of the idea of discretization, the two close values are classified into the same class. The equivalence relation corresponding to each attribute is defined based on this class-consistent. Li et al. [49] gave the process of constructing fuzzy Tcos-similarity relation for a fuzzy condition decision information system based on Gaussian kernel. The similarity of two objects under a single condition attribute is calculated through Gaussian kernel, then fuzzy Tcos-similarity relations under each condition attribute are formed. By using Hadamard product to aggregate these relations, a fuzzy Tcos-similarity relation under all condition attributes is obtained.

Although Zhang et al. [48] studied information structures and uncertainty measures for FFISs, their study was based on equivalence relations and ignored fuzziness of the system itself. This paper is also devoted to research measures of uncertainty for a FFIS, but unlike the idea of Zhang et al. the relation in a FFIS is based on Gaussian kernel, i.e., fuzzy Tcos-similarity relation. The fuzzy information structure is defined by using this relation so that it does not lose fuzziness. This is one of our research motivations. On the other hand, Li et al. [49] applied the fuzzy Tcos-similarity relation based on Gaussian kernel to construct rough sets, but their main purpose was to propose a multi-granulation decision-theoretic rough set method for fuzzy condition decision information systems. In this article, we try to give the fuzzy Tcos-similarity relation caused by a FFIS based on Gaussian kernel, the main purpose is to research uncertainty measures for this FFIS. This is another motivation of our research. The main advantage of proposed information structure is that it is based on fuzzy Tcos-similarity. Meanwhile, compared with the uncertainty measures based on general information structure, the proposed uncertainty measures also take fuzziness into account, so they can better reflect the essence of uncertainty and are more suitable for FFISs.

There are three main contributions in this article: (1) The fuzzy Tcos-similarity relation is extracted based on Gaussian kernel for a given FFIS. In view of this relation, fuzzy information granules are constructed, and fuzzy information structures based on these granules are advanced. (2) According to the advanced information structures, uncertainty measures are presented for a given FFIS. Besides, their important properties are given, and the relationships among these measures are established. (3) Two numerical experiments and statistical analysis (i.e., dispersion analysis and correlation analysis) of the proposed measures are conducted to determine their effectiveness.

These results will be helpful for understanding the uncertainty essence of FFISs. The proposed measures can be used to compute the importance of attributes, measure the quality of a decision rule in FFISs, construct the heuristic function in a heuristic reduction algorithm and so on.

The workflow of this article is displayed in Fig. 1.

The follow-up to this article consists of the following six sections. In Section 2, we review some basic knowledge about fuzzy sets, fuzzy relations and FFISs. The fuzzy Tcos-similarity relation is proposed based on Gaussian kernel method for a FFIS in Section 3. In Section 4, some notions for fuzzy information structures are given for a FFIS. In Section 5, we advance some indicators about measuring uncertainty for a FFIS. In Section 6, to assess the performance of the advanced measure indicators, two numerical experiments are conducted. Section 7 is a summary of this paper.

Section snippets

Preliminaries

In this paper, let U={e1,e2,,en} be a universe (non-empty finite set), I=[0,1]. IU is recorded as the family formed by all fuzzy sets on U.

Fuzzy Tcos-similarity relation caused by a FFIS

In this section, the fuzzy Tcos-similarity relation is proposed according to Gaussian kernel method in a FFIS.

Fuzzy information structures in a FFIS

In this section, some notions for fuzzy information structures are given for a FFIS.

Measuring uncertainty of a FFIS

In a FFIS, the information values corresponding to each attribute are fuzzy, i.e., each attribute ascertains a fuzzy set. In view of fuzzy information structures in a FFIS, we raise some indicators for measuring its uncertainty.

Numerical experiments and effectiveness analysis

To assess the performance of the advanced measure indicators in FFISs, we conduct two numerical experiments and analyze the effectiveness in this section. Firstly, two numerical experiments are conducted by considering the monotonicity of the four measures. Experimental results verify the monotonicity of four measures and show their availability. Then, dispersion degree is considered to analyze the effectiveness of the four measures. The performances of the four measures are compared based on

Conclusion

In this paper, we obtain fuzzy Tcos-similarity relation in a FFIS by using Gaussian kernel. In view of the fuzzy Tcos-similarity relation, some notions including dependence, information distance and inclusion degree for fuzzy information structures are given for a FFIS. According to this information structure, granulation measure of a given FFIS is advanced. Moreover, entropy measure is also considered for a given FFIS. To assess the performance of the advanced measure indicators, two numerical

CRediT authorship contribution statement

Zhaowen Li: Conceptualization, Methodology. Xiaofeng Liu: Investigation, Methodology, Writing - original draft, Writing - review & editing. Jianhua Dai: Supervision, Conceptualization, Validation, Writing - review & editing. Jiaolong Chen: Software, Investigation. Hamido Fujita: Validation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work is supported by the National Natural Science Foundation of China (61976089, 11971420), Natural Science Foundation of Guangxi, China (2016GXNSFAA380045, 2016GXNSFAA380282, 2016GXNSFAA380286), Key Laboratory of Optimization Control and Engineering Calculation in Department of Guangxi Education, China, Special Funds of Guangxi Distinguished Experts Construction Engineering, China, Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education,

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