Journal of Intelligent & Fuzzy Systems - Volume 37, issue 2

Authors: Chen, Ting | He, Sang-Sang | Wang, Jian-Qiang | Li, Lin | Luo, Hanyang

Article Type: Research Article

Abstract: Operations for linguistic neutrosophic numbers (LNNs) have been receiving considerable attention. Existing LNNs operations are generally based upon Archimedean triangular norm and triangular conorm. However, the existing operations fail to consider the correlation among variables. Archimedean copulas and co-copulas can not only reveal the correlation among variables but also prevent information loss when they are used as aggregation functions in the aggregation process. Here, LNN operations are redefined based on Archimedean copulas and co-copulas. Meanwhile, some specific cases are discussed. Then, a linguistic neutrosophic improved generalized weighted Choquet Heronian mean operator is developed. According to the proposed operator, a multi-criteria …decision-making method is proposed to tackle the selection problem of low-carbon suppliers. The influences of different generated functions and parameters are discussed, and the feasibility of the proposed method are validated through comparative analyses. Show more

Keywords: Linguistic neutrosophic sets, multiple criteria decision-making, Archimedean copulas and co-copulas, linguistic neutrosophic improved generalized weighted Choquet Heronian mean operator

DOI: 10.3233/JIFS-190041

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 2887-2912, 2019

Authors: Lai, Libang | Yang, Jie | Li, Dengfeng

Article Type: Research Article

Abstract: This paper proposes the concept of interval-valued average tree solution (“AT solution” for short) of graph cooperative games with interval-valued payoffs, and develop an effective and a direct simplified method for solving a subclass of interval-valued graph cooperative games. In this method, the interval-valued AT solution is proved to be a monotonic and non-decreasing function of coalitions’ values under specific condition. Hence, the lower and upper bounds of interval-valued AT solutions of graph cooperative games can be obtained directly by using the lower and upper bounds of the interval-valued coalitions’ payoffs, respectively. The proposed method gives better results than general …interval subtraction and the partial subtraction operator methods. In addition, some important properties of the interval-valued AT solutions of interval-valued graph cooperative games are discussed. At last, the applicability and superiority of the proposed approach is demonstrated by comparing with other methods. Show more

Keywords: Fuzzy game theory, interval-valued graph cooperative game, average tree solution, restricted coalition, interval computing

DOI: 10.3233/JIFS-190042

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 2913-2923, 2019

Authors: Gong, Kaixin | Chen, Chunfang | Wei, Ying

Article Type: Research Article

Abstract: Driven by the entrepreneurial trend of mass entrepreneurship and innovation, venture capital(VC) has been widely concerned and valued by investors. There is no doubt that investment decision plays a critical role in venture capital, however, due to the complexity of the investment environment, it is often difficult for investors to make a definite judgement on an innovative solution. Consequently, to express more accurately the hesitation and ambiguity of investors in the decision-making process, this paper proposes the probabilistic linguistic hesitant fuzzy preference relation(PLHFPR) based on the probabilistic linguistic hesitant fuzzy set(PLHFS). Unlike hesitant fuzzy preference relation (HFPR), PLHFPR not only …provides flexible linguistic expression for decision makers, but also gives the occurrence probability of each element in the PLHFPR. Considering that it is difficult for investors to give the exact probability of each element in the PLHFPR, a new probability calculation method is proposed based on the consistency analysis. What’s more, the convex consistency index(CCI) is defined to measure the consistency level of the PLHFPR by considering decision maker’s risk attitude. For the inconsistent PLHFPR, a weighted nonlinear programming model(WNPM) is constructed to derive an acceptable convex consistent PLHFPR and obtain the PLHFPR priority weight vector. Finally, an example about the venture capital is offered to verify the effectiveness of the proposed method. Show more

Keywords: Venture capital, Group decision making, PLHFPR, Consistency improvement, Weighted nonlinear programming model

DOI: 10.3233/JIFS-190052

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 2925-2936, 2019

Authors: Jamshidi, Mohammad | Saneie, Masoud | Mahmoodirad, Ali | Lotfi, Farhad Hoseinzadeh | Tohidi, Ghasem

Article Type: Research Article

Abstract: Data Envelopment Analysis (DEA) is recognized as a robust analytical tool extensively utilized in measuring the relative efficiency of a group of decision-making units (DMUs) with multiple inputs and outputs. The DEA models require inputs and outputs equipped with precise information. However, in real-world situations, inputs and outputs may be unstable and complicated, thus unable to be accurately measured. This problem resulted in the investigation of uncertain DEA models. The RUSSELL model was studied in this paper in an uncertain environment where uncertain inputs and outputs were belief degree-based uncertainty, useful for the cases for which no historical information of …an uncertain event is available. As the solution method, the uncertain RUSSELL model was converted to a crisp form using two approaches of expected value model and expected value and dependent chance-constrained model separately. Finally, an applied example regarding the Iranian banking system was presented to document the proposed models. Show more

Keywords: Data envelopment analysis, Uncertainty theory, New product, RUSSELL model, Iranian Bank

DOI: 10.3233/JIFS-190067

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 2937-2951, 2019

Authors: Khan, Abid | Zhu, Yuanguo

Article Type: Research Article

Abstract: The normal parameter reduction is used as a useful approach to identify the irrelevant parameters in soft set-based decision making systems. It finds a subset with least number of parameters that preserve the original classification of the decision alternatives. A number of algorithms have been developed for the normal parameter reduction of soft set but the case of repeated columns (i.e., e i  = e j ) was only considered by Danjuma et al. In this study, first we address the limitations of the Danjuma et al.’s approach to normal parameter reduction of soft set. Then, we propose a …new algorithm for normal parameter reduction of soft set which is free of all such limitations. Moreover, we compare the proposed algorithm with some of the existing algorithms of normal parameter reduction of soft set to show its efficiency. Finally, the application of the proposed algorithm is elaborated by a medical diagnostic problem. Show more

Keywords: Soft set, normal parameter reduction, decision making, medical diagnosis

DOI: 10.3233/JIFS-190071

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 2953-2968, 2019

Authors: Jain, Rachna | Alzubi, Jafar A. | Jain, Nikita | Joshi, Pawan

Article Type: Research Article

Abstract: Risk assessment is an important aspect of decision making while granting policy to an applicant. In the vast economy with enormous feature criteria for everyone, it is an ongoing challenge for the insurance companies to assess each applicant based on various factors to provide right policies on the basis of a risk score. We propose a method of ensemble learning as a solution to this problem where the predictions from pre-existing supervised learning algorithms can be used to enhance the accuracy of prediction. A real-world dataset having 128 attributes has been used to study the risk value associated with a …policy applicant. Machine learning algorithms were applied to the dataset to predict the risk associated with the applicant. Two ensembles have been used for classification of risk level assigned to a person which further leveraged our approach to an optimized and efficient class of predictors namely ANN and gradient boosting algorithm XGBoost. As a result, we discovered that the XGBoost algorithm with optimized hyperparameters gave us the best results in terms of Quadratic Weighted Kappa Score. The proposed methodology outperforms other existing methodologies as discussed in the later sections of the paper. Show more

Keywords: WEKA, cfsSubsetEval, bagging ensembles, XGBoost, artificial neural network

DOI: 10.3233/JIFS-190078

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 2969-2980, 2019

Authors: Zhang, Zhenyu | Lin, Jie | Miao, Runsheng | Zhou, Lixin

Article Type: Research Article

Abstract: As two important features of hesitant fuzzy linguistic term sets (HFLTSs), distance and similarity measures have been applied widely in many fields such as pattern recognition, decision making and prediction. Through analyzing the existing distance and similarity measures on HFLTSs, we find that they are not reasonable in some cases. Therefore, we first define the hesitance degree on HFLTSs to reflect the hesitant degree among several linguistic terms. On the basis of hesitance degree on HFLTSs, we develop several novel distance measures and further discuss their properties. Afterwards, several similarity measures based on hesitance degree are proposed and applied to …pattern recognition. By comparing our novel proposed distance and similarity measures with the existing methods and giving an example of pattern recognition, we prove that our proposed distance and similarity measures are more reliable than the previous method in some cases. Show more

Keywords: Hesitant fuzzy linguistic term sets, distance measure, similarity measure, hesitance degree, pattern recognition

DOI: 10.3233/JIFS-190082

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 2981-2990, 2019

Authors: Turan, Hasan Hüseyin | Kasap, Nihat | Delen, Dursun | Savran, Hüseyin

Article Type: Research Article

Abstract: Network providers and bandwidth brokers offer a variety of pricing policies based on differentiated quality-of-service (QoS) levels and volume discount schemes. In this paper, a cost minimization problem under various volume discount policies offered during the bandwidth allocation is formulated and solved via a heuristic algorithm. The proposed heuristic algorithm is based on fuzzy set theory. It has the capability of solving complex bandwidth provider selection and task allocation problems in telecommunications by considering a variety of volume discount policies offered by providers. The efficacy of the algorithm is tested under various scenarios to find the optimal strategies for firms …and to explore the suitability of the proposed approach. Show more

Keywords: Telecommunication, volume discount, optimization, heuristic, fuzzy QoS

DOI: 10.3233/JIFS-190085

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 2991-3015, 2019

Authors: Yan, Danfeng | Guo, Zhengkai

Article Type: Research Article

Abstract: Collaborative filtering (CF) has achieved great performance in recommender system over past decades. CF-based methods firstly map users and items to latent factors which share the same latent space, and then use a linear function to predict user ratings on items, such as inner product or cosine distance. It only uses original latent feature, however feature interactions are usually helpful in enhancing recommendation performance. To tackle such issue, we used Factorization Machines (FM) to enhanced linear methods by incorporating the second-order feature interactions. In this paper, we propose a novel hybrid model, AutoFM, which combine Denoising Autoencoder (DAE) and FM …together. AutoFM follows collaborative filtering method, it firstly uses DAE to map users and items to latent factor, then it uses FM calculating user ratings on items. To tackle the cold start problem, we also take as the input of FM user’s and item’s side information besides of latent factor. We conduct AutoFM on three real-world datasets, and the experiment results show that AutoFM consistently outperforms the state-of-the-art method. Show more

Keywords: AutoFM, collaborative filtering, recommender system, autoencoders, factorization machine

DOI: 10.3233/JIFS-190099

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 3017-3025, 2019

Authors: Zhu, Danchen | Zhang, Yongxiang | Zhao, Lei

Article Type: Research Article

Abstract: Features of raw bearing vibration signals aren’t invariant with the change of rotating speed. As a result, determining the proper features is essential for the feature learning based intelligent fault diagnosis method for rolling element bearing with varying rotating speed. To address this issue, a convolutional neural network (CNN) based fault diagnosis approach is proposed. In the proposed method, envelope order spectra extracted from the raw vibration signals are used to provide abundant information about the fault characteristic orders, which are features invariant to the rotating speed. Subsequently, to extract these representative features automatically, a CNN model is constructed and …employed, which avoid the manual feature selection. Finally, the type of bearing defects can be recognized successfully. In the experimental verification, the CNN is trained using a data set corresponds to one revolution per minute (RPM), while the data sets correspond to other RPMs are employed to verify the classification accuracy of the trained CNN, which can reflect the effectiveness of proposed method for bearing fault detection under different rotating speed. Experimental results show the satisfactory performance of fault-pattern recognition for the proposed method. When compared with some other approaches using intelligence-based fault diagnosis method, the results show the superiority of the proposed method. Show more

Keywords: Convolutional neural network, envelope order spectrum, rolling element bearing, fault diagnosis

DOI: 10.3233/JIFS-190101

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 3027-3040, 2019

Authors: Shi, Zhan-Hong | Zhang, Ding-Hai

Article Type: Research Article

Abstract: Measuring the similarity between images is an essential problem in various image processing and pattern recognition applications. In pattern recognition problems, it is indispensable to give formulas for calculating similarity between different patterns. But it is very difficult to find a certain measure that can be successfully applied to all kinds of pattern recognition problems. Intuitionistic fuzzy sets have been successfully applied to various areas such as pattern recognition and medical diagnostics. In intuitionistic fuzzy sets theory, the calculation of the similarity between intuitionistic fuzzy sets is a significant technique for distinguishing the similarity degree between intuitionistic fuzzy sets. The …existing similarity measures almost are obtained in the sense of distance. In this paper, we present a novel way to obtain the similarity measure between intuitionistic fuzzy sets from a new perspective. Our main purpose is to show that according to the membership and non-membership functions of intuitionistic fuzzy sets, a triangular norm can induce an inclusion degree. Using this triangular norm and the induced inclusion degree, a similarity measure of intuitionistic fuzzy sets can be obtained. We also prove some properties of the proposed similarity measure between intuitionistic fuzzy sets. As the applications of similarity degree proposed in this paper, we first present an intuitionistic fuzzy clustering algorithm based on similarity degree. Then, the similarity degree proposed in this paper is applied to pattern recognition. At the same time, the numerical examples are employed to illustrate the effectiveness of proposed method. Show more

Keywords: Intuitionistic fuzzy sets, Similarity measure, Triangular norm, Fuzzy clustering, Pattern recognition

DOI: 10.3233/JIFS-190102

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 3041-3051, 2019

Authors: She, Yanhong | Wang, Wei | He, Xiaoli | Du, Yan | Liu, Yaoyao

Article Type: Research Article

Abstract: Formal concept analysis, originally proposed by Wille, is a mathematical tool to analyse and represent data in the form of complete formal context. However, in situations with incomplete information, one only has partial knowledge about a concept, recently, a common conceptual framework of the notions of interval sets and incomplete formal contexts for representing partially-known concepts were presented. In this study, we examine and reinterpret the existing studies on partially known concepts by means of three-valued logics. By treating an incomplete formal context as a three-valued formal context and considering the one-to-one correspondence between interval sets and three-valued mappings, we …investigate the condition under which the four types of partially known concepts can be generated by using three-valued implication operators. Moreover, we also evaluate the role of three-valued logic in characterizing attribute implications. A sufficient and necessary condition for computing the true value of an implication correctly in the sense of Kriple semantics is provided. Show more

Keywords: Formal context, partially-known concept, three-valued logic, attribute implication

DOI: 10.3233/JIFS-190111

Citation: Journal of Intelligent & Fuzzy Systems, vol. 37, no. 2, pp. 3053-3064, 2019