Showing papers in "Journal of intelligent systems in 2016"

A New Generalized Pythagorean Fuzzy Information Aggregation Using Einstein Operations and Its Application to Decision Making

Abstract: The objective of this article is to extend and present an idea related to weighted aggregated operators from fuzzy to Pythagorean fuzzy sets PFSs. The main feature of the PFS is to relax the condition that the sum of the degree of membership functions is less than one with the square sum of the degree of membership functions is less than one. Under these environments, aggregator operators, namely, Pythagorean fuzzy Einstein weighted averaging PFEWA, Pythagorean fuzzy Einstein ordered weighted averaging PFEOWA, generalized Pythagorean fuzzy Einstein weighted averaging GPFEWA, and generalized Pythagorean fuzzy Einstein ordered weighted averaging GPFEOWA, are proposed in this article. Some desirable properties corresponding to it have also been investigated. Furthermore, these operators are applied to decision-making problems in which experts provide their preferences in the Pythagorean fuzzy environment to show the validity, practicality, and effectiveness of the new approach. Finally, a systematic comparison between the existing work and the proposed work has been given.

Fundamental Properties of Interval-Valued Pythagorean Fuzzy Aggregation Operators

Abstract: In this paper, we investigate the multiple attribute group decision making MAGDM problems with interval-valued Pythagorean fuzzy sets IVPFSs. First, the concept, operational laws, score function, and accuracy function of IVPFSs are defined. Then, based on the operational laws, two interval-valued Pythagorean fuzzy aggregation operators are developed for aggregating the interval-valued Pythagorean fuzzy information, such as interval-valued Pythagorean fuzzy weighted average IVPFWA operator and interval-valued Pythagorean fuzzy weighted geometric IVPFWG operator. A series of inequalities of aggregation operators are studied. Later, we develop some interval-valued Pythagorean fuzzy point operators. Moreover, combining the interval-valued Pythagorean fuzzy point operators with IVPFWA operator, we present some interval-valued Pythagorean fuzzy point weighted averaging IVPFPWA operators, which can adjust the degree of the aggregated arguments with some parameters. Then, we propose an interval-valued Pythagorean fuzzy ELECTRE method to solve uncertainty MAGDM problem. Finally, an illustrative example for evaluating the software developments is given to verify the developed approach and to demonstrate its practicality and effectiveness.

A Novel Approach Based on Similarity Measure for Pythagorean Fuzzy Multiple Criteria Group Decision Making

Abstract: The Pythagorean fuzzy set, as a new extension of intuitionistic fuzzy set, has recently been developed to manage the complex uncertainty in practical group decision problems. The purpose of this article is to develop a new decision method based on similarity measure to address multiple criteria group decision making problems within Pythagorean fuzzy environment based on Pythagorean fuzzy numbers PFNs. The contribution of this article is fivefold: 1 An accuracy function of PFNs is defined and a new ranking method for PFNs is proposed; 2 new Pythagorean fuzzy aggregating operators are developed; 3 a novel similarity measure for PFNs is presented, and some desirable properties are discussed; 4 a simple and effective Pythagorean fuzzy group decision method is introduced; and 5 The proposed method is applied to address the selection problem of photovoltaic cells.

A Novel Correlation Coefficients between Pythagorean Fuzzy Sets and Its Applications to Decision-Making Processes

Abstract: Pythagorean fuzzy set PFS is one of the most successful in terms of representing comprehensively uncertain and vague information. Considering that the correlation coefficient plays an important role in statistics and engineering sciences, in this paper, after pointing out the weakness of the existing correlation coefficients between intuitionistic fuzzy sets IFSs, we propose a novel correlation coefficient and weighted correlation coefficient formulation to measure the relationship between two PFSs. Pairs of membership, nonmembership, and hesitation degree as a vector representation with the two elements have been considered during formulation. Numerical examples of pattern recognition and medical diagnosis have been taken to demonstrate the efficiency of the proposed approach. Results computed by the proposed approach are compared with the existing indices.

Pythagorean Fuzzy Choquet Integral Based MABAC Method for Multiple Attribute Group Decision Making

Abstract: In this paper, we define the Choquet integral operator for Pythagorean fuzzy aggregation operators, such as Pythagorean fuzzy Choquet integral average PFCIA operator and Pythagorean fuzzy Choquet integral geometric PFCIG operator. The operators not only consider the importance of the elements or their ordered positions but also can reflect the correlations among the elements or their ordered positions. It is worth pointing out that most of the existing Pythagorean fuzzy aggregation operators are special cases of our operators. Meanwhile, some basic properties are discussed in detail. Later, we propose two approaches to multiple attribute group decision making with attributes involving dependent and independent by the PFCIA operator and multi-attributive border approximation area comparison MABAC in Pythagorean fuzzy environment. Finally, two illustrative examples have also been taken in the present study to verify the developed approaches and to demonstrate their practicality and effectiveness.

Symmetric Pythagorean Fuzzy Weighted Geometric/Averaging Operators and Their Application in Multicriteria Decision-Making Problems

Abstract: Pythagorean fuzzy sets PFSs, originally proposed by Yager, are a new tool to deal with vagueness with the square sum of the membership degree and the nonmembership degree equal to or less than 1, which have much stronger ability than Atanassov's intuitionistic fuzzy sets to model such uncertainty. In this paper, we modify the existing score function and accuracy function for Pythagorean fuzzy number to make it conform to PFSs. Associated with the given operational laws, we define some novel Pythagorean fuzzy weighted geometric/averaging operators for Pythagorean fuzzy information, which can neutrally treat the membership degree and the nonmembership degree, and investigate the relationships among these operators and those existing ones. At length, a practical example is provided to illustrate the developed operators and to make a comparative analysis.

Ambient Assisted Living Technologies for Aging Well: A Scoping Review

Abstract: Abstract Ambient assisted living (AAL) technology is of considerable interest in supporting the independence and quality of life of older adults. As such, it is a core focus of the emerging field of gerontechnology, which considers how technological innovation can aid health and well-being in older age. For this scoping review, a comprehensive search of databases and key journals was conducted from January to April of 2013 in order to identify AAL technologies that have the potential to help deal with some of the challenges associated with aging. In particular, we focused on technologies that could potentially be used by people living with some degree of cognitive impairment, ranging from normal cognitive aging to mild cognitive impairment up to earlier stages of dementia. Options currently available and those still under development were both included in our search. Fifty-nine technologies were identified and are outlined here, along with a discussion of history of AAL from a gerontological perspective and related theoretical considerations.

The Properties of Continuous Pythagorean Fuzzy Information

Abstract: In practical decision-making processes, we can utilize various types of fuzzy sets to express the uncertain and ambiguous information. However, we may encounter such the situations: the sum of the support membership degree and the against nonmembership degree to which an alternative satisfies a criterion provided by the decision maker may be bigger than 1 but their square sum is equal to or less than 1. The Pythagorean fuzzy sets PFS, as the generalization of the fuzzy sets, can be used to effectively deal with this issue. Therefore, to enrich the theory of PFS, it is very necessary to investigate the fundamental properties of Pythagorean fuzzy information. In this paper, we first describe the change values of Pythagorean fuzzy numbers PFNs, which are the basic components of PFSs, when considering them as variables. Then we divide all the change values into the eight regions by using the basic operations of PFNs. Finally, we develop several Pythagorean fuzzy functions and study their fundamental properties such as continuity, derivability, and differentiability in detail.

New Distance Measure of Single-Valued Neutrosophic Sets and Its Application

Abstract: A single-valued neutrosophic set SVNS is an instance of a neutrosophic set, which can be used to handle uncertainty, imprecise, indeterminate, and inconsistent information in real life. In this paper, a new distance measure between two SVNSs is defined by the full consideration of truth-membership function, indeterminacy-membership function, and falsity-membership function for the forward and backward differences. Then the similarity measure, the entropy measure, and the index of distance are also presented. Finally, two illustrative examples are given to demonstrate the effectiveness of the proposed clustering method and multicriteria decision-making method based on the distance similarity measure between SVNSs.

Pythagorean Fuzzy Multigranulation Rough Set over Two Universes and Its Applications in Merger and Acquisition

Abstract: Pythagorean fuzzy set, an extension form of intuitionistic fuzzy set, which owns many advantages for dealing with uncertainties, and it has been developed to deal with various complex decision-making problems. Furthermore, based on lower and upper approximations induced by multiple binary relations, the multigranulation rough set has become one of the most promising directions in rough set theory. To combine the two ideas and explore the practical decision-making problems, we develop a new multigranulation rough set model, called Pythagorean fuzzy multigranulation rough set over two universes. In the framework of our study, we introduce the models of Pythagorean fuzzy rough set over two universes and Pythagorean fuzzy multigranulation rough set over two universes, respectively. Both the definition and basic properties are explored. Finally, we give a general algorithm, which is applied to a decision-making problem in merger and acquisition, and the effectiveness of the algorithm is demonstrated by a numerical example.

A Multigranularity Linguistic Group Decision-Making Method Based on Hesitant 2-Tuple Sets

Abstract: Hesitant fuzzy linguistic term set HFLTS is a very useful technology in dealing with decision-making problems where people have hesitancy in providing their linguistic assessments. Distinct methods have been developed to aid decision making with HFLTSs, yet there is little research involving the issue that how to deal with the multigranularity hesitant fuzzy linguistic information. The aim of this paper is to develop the aggregation method for multigranularity hesitant fuzzy linguistic information and solve the linguistic group decision problem with different linguistic term sets. To do so, we first modify the translation functions and aggregation operators in the existing 2-tuple linguistic representation models so as to aggregate linguistic terms from different linguistic term sets. Then, we introduce the notion of hesitant 2-tuple sets to make computation of HFLTSs without loss of information, and develop some new operators to aggregate HFLTSs from different linguistic term sets. Using these operators, we propose a method to deal with multigranularity linguistic group decision-making problems with different situations where importance weights of either criteria or experts are known or unknown. Finally, the multigranularity linguistic group decision-making model is implemented to the healthcare waste treatment in West China Hospital to validate its effectiveness and efficiency in aiding decision-making process.

Practical Methods for Constructing Possibility Distributions

Abstract: This survey paper provides an overview of existing methods for building possibility distributions. We both consider the case of qualitative possibility theory, where the scale remains ordinal, and the case of quantitative possibility theory, where the scale is the real interval [0, 1]. Methods may be order-based or similarity-based for qualitative possibility distributions, whereas statistical methods apply in the quantitative case and then possibilities encode nested random epistemic sets or upper bounds of probabilities. But distance-based approaches, or expert estimates, may be also exploited in the quantitative case.

A Note on Extension of TOPSIS to Multiple Criteria Decision Making with Pythagorean Fuzzy Sets

Abstract: In this note, we point out an error to the proof of Theorem 3.4 in Zhang and Xu Int J Intell Syst 2014;2912:1061-1078 by a counterexample. We find that the inequality i.e., |πβ12-πβ22|i¾?|πβ12-πβ32| with respect to the degrees of indeterminacy of any three Pythagorean fuzzy numbers in the proof of Theorem 3.4 in Zhang and Xu's paper is not valid. A new proof is provided in this note.

Multiple-Attribute Group Decision-Making Method under a Neutrosophic Number Environment

Abstract: As a neutrosophic number, which consists of a determinate part and an indeterminate part, can more easily and better express incomplete and indeterminate information that exists commonly in real situations, the main purposes of this paper are to provide a neutrosophic number tool for group decision-making problems with indeterminate information under a neutrosophic number environment and to develop a de-neutrosophication method and a possibility degree ranking method for neutrosophic numbers from the probability viewpoint as a methodological support for group decision-making problems. In group decisionmaking problems with neutrosophic numbers, through the de-neutrosophication and possibility degree ranking order of neutrosophic numbers, the ranking order of alternatives is performed well as the possibility degree ranking method has the intuitive meaning from the probability viewpoint, and then the best one(s) can be determined as well. Finally, two illustrative examples show the applications and effectiveness of the proposed method.

An Intuitionistic 2-Tuple Linguistic Information Model and Aggregation Operators

Abstract: Dealing with uncertainty is always a challenging problem, and different tools have been proposed to deal with it. Fuzzy sets was presented to manage situations in which experts have some membership value to assess an alternative. The fuzzy linguistic approach has been applied successfully to many problems. The linguistic information expressed by means of 2-tuples, which were composed by a linguistic term and a numeric value assessed in [ - 0.5, 0.5. Linguistic values was used to assess an alternative and variable in qualitative settings. Intuitionistic fuzzy sets were presented to manage situations in which experts have some membership and nonmembership value to assess an alternative. In this paper, the concept of an I2LI model is developed to provide a linguistic and computational basis to manage the situations in which experts assess an alternative in possible and impossible linguistic variable and their translation parameter. A method to solve the group decision making problem based on intuitionistic 2-tuple linguistic information I2LI by the group of experts is formulated. Some operational laws on I2LI are introduced. Based on these laws, new aggregation operators are introduced to aggregate the collective opinion of decision makers. An illustrative example is given to show the practicality and feasibility of our proposed aggregation operators and group decision making method.

Folksonomy-Based Recommender Systems: A State-of-the-Art Review

Abstract: Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content e.g., Web pages, pictures, and video using open-ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy-based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and link-analysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines.

Accuracy Comparison of Analogy-Based Software Development Effort Estimation Techniques

Abstract: Estimation by analogy is a commonly used software effort estimation technique and a suitable alternative to other conventional estimation techniques: It predicts the effort of the target project using information from former similar projects. While it is relatively easy to handle numerical attributes, dealing with categorical attributes is one of the most difficult issues for analogy-based estimation techniques. Therefore, we propose, in this paper, a novel analogy-based approach, called 2FA-kprototypes, to predict effort when software projects are described by a mix of numerical and categorical attributes. To this aim, the well-known fuzzy k-prototypes algorithm is integrated into the process of estimation by analogy. The estimation accuracy of 2FA-kprototypes was evaluated and compared with that of two techniques: 1 classical analogy-based technique and 2 2FA-kmodes, which is a technique that we have developed recently. The comparison was performed using four data sets that are quite diverse and have different sizes: ISBSG, COCOMO, USP05-FT, and USP05-RQ. The results obtained showed that both 2FA-kprototypes and 2FA-kmodes perform better than classical analogy.

Fuzzy Linguistic Induced Generalized OWA Operator and Its Application in Fuzzy Linguistic Decision Making

Abstract: With respect to multiple attribute group decision making problems, in which the attribute values take the form of fuzzy linguistic scale variables, some decision analysis approaches are proposed. In this paper, we introduce the fuzzy linguistic induce generalized ordered weighted averaging operator and study some of its main properties by utilizing some operational laws of fuzzy linguistic scale variables. We end the paper with a numerical example of the new approach in a fuzzy linguistic decision making.

Employing Emotion Cues to Verify Speakers in Emotional Talking Environments

Abstract: Usually, people talk neutrally in environments where there are no abnormal talking conditions such as stress and emotion. Other emotional conditions that might affect people talking tone like happiness, anger, and sadness. Such emotions are directly affected by the patient health status. In neutral talking environments, speakers can be easily verified, however, in emotional talking environments, speakers cannot be easily verified as in neutral talking ones. Consequently, speaker verification systems do not perform well in emotional talking environments as they do in neutral talking environments. In this work, a two-stage approach has been employed and evaluated to improve speaker verification performance in emotional talking environments. This approach employs speaker emotion cues (text-independent and emotion-dependent speaker verification problem) based on both Hidden Markov Models (HMMs) and Suprasegmental Hidden Markov Models (SPHMMs) as classifiers. The approach is comprised of two cascaded stages that combines and integrates emotion recognizer and speaker recognizer into one recognizer. The architecture has been tested on two different and separate emotional speech databases: our collected database and Emotional Prosody Speech and Transcripts database. The results of this work show that the proposed approach gives promising results with a significant improvement over previous studies and other approaches such as emotion-independent speaker verification approach and emotion-dependent speaker verification approach based completely on HMMs.

Automatic Data Clustering Using Parameter Adaptive Harmony Search Algorithm and Its Application to Image Segmentation

Abstract: Abstract In this paper, the problem of automatic data clustering is treated as the searching of optimal number of clusters so that the obtained partitions should be optimized. The automatic data clustering technique utilizes a recently developed parameter adaptive harmony search (PAHS) as an underlying optimization strategy. It uses real-coded variable length harmony vector, which is able to detect the number of clusters automatically. The newly developed concepts regarding “threshold setting” and “cutoff” are used to refine the optimization strategy. The assignment of data points to different cluster centers is done based on the newly developed weighted Euclidean distance instead of Euclidean distance. The developed approach is able to detect any type of cluster irrespective of their geometric shape. It is compared with four well-established clustering techniques. It is further applied for automatic segmentation of grayscale and color images, and its performance is compared with other existing techniques. For real-life datasets, statistical analysis is done. The technique shows its effectiveness and the usefulness.

Application of Optimistic and Pessimistic OWA and DEA Methods in Stock Selection

Abstract: One of the main objectives of fund managers in financial service industry is to select superior stocks by analyzing financial ratios. This paper proposes a novel methodology for stock selection by integrating optimistic and pessimistic ordered weighted averaging OWA and data envelopment analysis DEA methods. The paper first reveals the drawback of using the standard DEA models for stocks evaluation and then proposes a new method by using the OWA operator. Unlike the classical DEA, the proposed method in this paper does not involve the specification of inputs and outputs. The paper incorporates optimistic and pessimistic scenarios and generates interval OWA scores for all stocks. This is followed by using appropriate interval DEA models for selecting superior stocks. The proposed method in this paper is applied to identify high financial performance stocks in the Tehran stock market.

Telehealth Monitoring of Patients in the Community

Abstract: This article outlines a decision support system that seeks to help community nurses monitor the well-being of their chronically ill patients. It is designed for nurses to stay in contact with their patients without spending unnecessary time on less productive aspects of community nursing, such as avoidable driving to and from patients ' houses and taking measurements of vital signs to assess their health condition. It therefore allows the nurse to spend more time on managing the factors that could lead to a healthier patient. The decision support system is developed for two levels of mathematical capability. Nurses with a statistical background are provided with in-depth information allowing them to detect changes in mean, mean square error (and hence variation), and correlations using a variation on dynamic principle components. Less math- ematically inclined nurses are offered information about trends, change points, and a simpler multivariate view of a patient ' s well-being involving parallel coordinate plots.

GHFHC: Generalized Hesitant Fuzzy Hierarchical Clustering Algorithm

Abstract: Dealing with uncertainty is an undeniable challenge in the real-world problems. In this paper, we focus on hesitant environment such as generalized hesitant fuzzy sets introduced by Qian eti¾?al. So we propose a new generalized hesitant fuzzy hierarchical clustering GHFHC algorithm based on Atanassov's intuitionistic fuzzy set theory. We extend conventional hierarchical clustering, which just works on the crisp data, and introduce a clustering algorithm, which can be applied on large data set with generalized hesitant fuzzy data. The run time of the GHFHC algorithm shows that its computational complexity will be low. Also, the GHFHC algorithm produces the clusters with arbitrary shapes by using the various distance measures. Finally, an example is provided to illustrate the practicality of the proposed algorithm.

Group Decision Making in Information Systems Security Assessment Using Dual Hesitant Fuzzy Set

Abstract: Network information system security has become a global issue since it is related to the economic development and national security. Information system security assessment plays an important role in the development of security solutions. Aiming at this issue, a dual hesitant fuzzy DHF group decision-making GDM method was proposed in this paper to assist the assessment of network information system security. A systemic index containing four aspects was established including organization security, management security, technical security, and personnel management security. The DHF group evaluation matrix was constructed based on the individual evaluation information from each expert. Some power average operator-based DHF information aggregation operators are proposed and used to fusion the performance of each criterion for information systems. The advantage of these operators is that they can describe the relationship between the indexes quantitatively. Finally, a case study about information systems security assessment was presented to verify the effectiveness of proposed GDM methods.

Gaussian Mixture Model Based Classification of Stuttering Dysfluencies

Abstract: Abstract The classification of dysfluencies is one of the important steps in objective measurement of stuttering disorder. In this work, the focus is on investigating the applicability of automatic speaker recognition (ASR) method for stuttering dysfluency recognition. The system designed for this particular task relies on the Gaussian mixture model (GMM), which is the most widely used probabilistic modeling technique in ASR. The GMM parameters are estimated from Mel frequency cepstral coefficients (MFCCs). This statistical speaker-modeling technique represents the fundamental characteristic sounds of speech signal. Using this model, we build a dysfluency recognizer that is capable of recognizing dysfluencies irrespective of a person as well as what is being said. The performance of the system is evaluated for different types of dysfluencies such as syllable repetition, word repetition, prolongation, and interjection using speech samples from the University College London Archive of Stuttered Speech (UCLASS).

A Behavioral Analysis of WOWA and SUOWA Operators

Abstract: Weighted ordered weighted averaging WOWA and semiuninorm-based ordered weighted averaging SUOWA operators are two families of aggregation functions that simultaneously generalize weighted means and OWA operators. Both families can be obtained by using the Choquet integral with respect to normalized capacities. Therefore, they are continuous, monotonic, idempotent, compensative, and homogeneous of degree 1 functions. Although both families fulfill good properties, there are situations where their behavior is quite different. The aim of this paper is to analyze both families of functions regarding some simple cases of weighting vectors, the capacities from which they are building, the weights affecting the components of each vector, and the values they return.

Geometric Bonferroni Mean Operators

Abstract: The Bonferroni mean has been extensively applied in multicriteria decision-making and support system and developed intuitionistic fuzzy set theory. Based on the second interpretation of the Bonferroni mean, in this paper, we introduce the geometric Bonferroni mean, which is a generalization of the Bonferroni mean and geometric mean and generalized geometric Bonferroni mean, and investigate their properties. To describe the uncertainty and fuzziness more objectively, we further develop the intuitionistic fuzzy geometric Bonferroni mean and the generalized intuitionistic fuzzy geometric Bonferroni mean, which describe the relationship between arguments, and the weighted intuitionistic fuzzy geometric Bonferroni mean and the generalized weighted intuitionistic fuzzy geometric Bonferroni mean, which consider the importance of each argument. Finally, we investigate their properties in detail.

A New Probability Transformation Based on the Ordered Visibility Graph

Abstract: One of the key issues in the application of the Dempster-Shafer evidence theory is the transformation between basic probability assignments BPAs and probability. In this paper, new probability transformation based on the ordered visibility graph OVG is proposed to solve the decision-making problem. In the proposed transformation, an OVG can be constructed based on the BPAs. From this OVG, the network of focal elements can be obtained. The degree of a node in the network represents its weight, which is essential to the transformation. Based on these weights, the probability results are obtained. Some illustrative cases are provided to demonstrate the effectiveness of the proposed probability transformation.

On Typical Hesitant Fuzzy Prioritized or Operator in Multi-Attribute Decision Making

Abstract: Since hesitant fuzzy set was proposed, multi-attribute decision making MADM with hesitant fuzzy information, which is also called hesitant fuzzy MADM, has been a hot research topic in decision theory. This paper investigates a special kind of hesitant fuzzy MADM problems in which the decision data are expressed by several possible values, and the evaluative attributes are in different priority levels. Firstly, we introduce the definitions of hesitant fuzzy t-norm and t-conorm by extending the notions of t-norm and t-conorm to the hesitant fuzzy environment and explore their constructions by means of t-norms and t-conorms. Then motivated by the prioritized "or" operator R. R. Yager, Prioritized aggregation operators, International Journal of Approximate Reasoning 2008;48:263-274, we develop the typical hesitant fuzzy prioritized "or" operator based on the developed hesitant fuzzy t-norms and t-conorms. In this operator, the degree of satisfaction of each alternative in each priority level is derived from a hesitant fuzzy t-conorm to preserve trade-offs among the attributes in the same priority level, and the priority weights of attributes are induced by a hesitant fuzzy t-norm to model the prioritization relationship among attributes. Furthermore, we apply the developed typical hesitant fuzzy prioritized "or" operator to solving the MADM problems in which the decision data are expressed by several possible values and the attributes are in different priority levels. In addition, two numerical examples are given to, respectively, illustrate the applicability and superiority of the developed aggregation operator by comparative analyses with previous research.

On Legitimacy Feedback Mechanisms in Agent-Based Modeling of Civil Violence

Abstract: Epstein's agent-based model ABM of civil violence has been very popular and successful due to its formulation soundness, simplicity, and explanatory power. Variants of this model have been proposed for the simulation of different types of social conflict phenomena worker protest, riots, or urban crime and for investigating the effect of mechanisms that are not considered in the original model. In a previous work, we introduced imprisonment delay, "news impact," and legitimacy feedback effects in Epstein's ABM of civil violence. In this paper, we focused specifically on improving the formulation of legitimacy feedback. In the model presented herein, legitimacy varies as a function of subindicators identified in theoretical studies on legitimacy measurement. We considered four different functions for expressing the legitimacy-weighted average, geometric mean, exponentially decaying "system support," and "justification"-and two different feedback mechanisms: homogeneous global perceived legitimacy and heterogeneous agent-dependent perceived legitimacy. It was found that, for certain combinations of input parameters, the present model produced solutions with an initial period of calm with small bursts of rebellion, followed by a sudden large-scale rebellion after which intermittent bursts of rebellion occur as in Epstein's model, where legitimacy drops never returning to the initial level. These results provide an alternative explanation of the mechanisms by which apparently stable authoritarian regimes, when facing an unexpected large-scale uprising, respond with repression and afterward struggle with intermittent bursts of rebellion because they are perceived as illegitimate. The present model can also be used to test theories on the aggregation of legitimacy indicators in a global legitimacy score.