Journal of Intelligent & Fuzzy Systems - Volume 33, issue 1

Authors: Yuan, Guoqiang | Tian, Yi | Wang, Shuming

Article Type: Research Article

Abstract: This paper introduces the fuzzy Value-at-Risk (VaR) into crop production planning and proposes a risk-based decision-making approach to the problem in imprecise or fuzzy parameters environments. In the proposed fuzzy crop production planning VaR model, the profit coefficients are imprecise uncertain and assumed to be fuzzy variables with known possibility distributions. Due to the fuzzy variable parameters with infinite supports, the VaR model is inherently an infinite-dimensional optimization problem that can rarely be solved directly via conventional mathematical programming methods. Therefore, algorithm procedures for solving this optimization problem must rely on approximation schemes and heuristic computing. The paper presents a …heuristic algorithm, which integrates approximation approach (AA), neural network (NN) and genetic algorithm (GA), to solve the fuzzy crop production planning VaR model. Finally, a practical example is given to show the feasibility and effectiveness of the proposed model and heuristic algorithm. Show more

Keywords: Fuzzy programming, Value-at-Risk (VaR), production planning, approximation approach, heuristic algorithm

DOI: 10.3233/JIFS-15982

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 1, pp. 1-14, 2017

Authors: Xie, Ningxin | Li, Zhaowen | Zhang, Gangqiang

Article Type: Research Article

Abstract: Intuitionistic fuzzy sets and soft sets describe the different types of uncertainty. Their fusion gets intuitionistic fuzzy soft sets, forms a more powerful mathematical tool for uncertainty description and further enlarges the scope of applications. This is more advantageous to solve decision-making problems. This paper proposes an intuitionistic fuzzy soft set method for stochastic decision-making applying prospect theory and grey relational analysis. According to the known evaluation information, this method describes stochastic decision-making problems as intuitionistic fuzzy soft sets. Firstly, a score function is defined and intuitionistic fuzzy numbers are converted the values of this score function. Secondly, the prospect …decision matrix is given by utilizing the prospect value formula. Thirdly, the weight of each parameter is determined through grey relational analysis. Fourthly, the comprehensive prospect value of each scheme is gotten on the basis of the weight of each parameter. Fifthly, the optimal choice is obtained according to the comprehensive prospect values. Sixthly, an algorithm is presented. Finally, two applied examples are employed to illustrate the effectiveness and feasibility of this method. Show more

Keywords: Intuitionistic fuzzy soft set, decision-making, prospect theory, grey relational analysis, score function

DOI: 10.3233/JIFS-16013

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 1, pp. 15-25, 2017

Authors: Xu, Genjiu | Li, Xianghui | Sun, Hao | Su, Jun

Article Type: Research Article

Abstract: In the field of cooperative games there is an extensive literature that studies various situations of cooperation. Myerson (1977) introduced the communication structure which is an undirected graph describing the bilateral relationships among the players and the Myerson value of a game is obtained by taking the Shapley value of an auxiliary graph game on communication structure. Aubin (1981) proposed fuzzy cooperative games in which players have the possibility to cooperate with different participation levels. In this paper we consider cooperative games on communication structure with fuzzy coalition. The Myerson value and its individual rational revision are defined as the …Shapley value of newly auxiliary graph games and discussed based on Choquet integral form and proportional form respectively. They are also characterized in terms of some extended component efficiency and fairness. Furthermore, by showing that the Myerson value is a fuzzy core allocation, the non-emptiness of the fuzzy core is verified for a graph game on communication structure with fuzzy coalition. Show more

Keywords: Fuzzy cooperative game, communication structure, Myerson value, individual rational, fuzzy core

DOI: 10.3233/JIFS-16080

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 1, pp. 27-39, 2017

Authors: Xu, Yejun | Wang, Huimin

Article Type: Research Article

Abstract: Hesitant fuzzy linguistic term sets suit the modelling of qualitative setting so that experts provide several possible linguistic values than a single term for an indicator, alternative, variable, etc. This paper proposes a consistency and consensus based decision support model for group decision making with hesitant 2-tuple fuzzy linguistic preference relations (H2TFLPRs). A revised definition of hesitant fuzzy linguistic preference relation (HFLPR) is presented. In order to measure the consensus degrees, two indexes, an individual to group consensus index (ICI ) and a group consensus index (GCI ) are introduced, respectively. A consistency control process is developed to transform an …unacceptable consistency H2TFLPR to an acceptable one. Then, an iterative group consensus reaching process is designed to conduct the decision makers (DMs) to achieve the predefined consensus level. In the consistency and consensus improving process, both the feedback mechanism and no-feedback mechanism are devised to conduct the DMs how to modify their original judgments, and both mechanisms can retain the DMs’ original information as much as possible. Finally, a numerical example is illustrated to demonstrate the effectiveness of the proposed method. Show more

Keywords: Hesitant 2-tuple fuzzy linguistic preference relation, additive consistency, consensus, group decision support model

DOI: 10.3233/JIFS-161029

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 1, pp. 41-54, 2017

Authors: Zhongguo, Yang | Hongqi, Li | Liping, Zhu | Qiang, Liu | Ali, Sikandar

Article Type: Research Article

Abstract: The k-nearest-neighbor classifier is a vital algorithm. In practice, the choice of k is decided by the cross-validation method. We propose a new method for neighborhood size selection based on the data set profile. The distribution of a data set and its intrinsic characteristics are the fundamental factors to the choice of k. A local complexity was computed for each example and a complexity profile was constructed by sorting these local complexity values which try to capture inner structure of a data set. After this, a feature vector was built by combing the local complexity profile and some statistic features …of a data set. In addition, a history meta-data set was constructed by using the feature vector as attributes and the optimum k value of data set as the label, which was calculated by using ten cross-validation methods. A predict model was trained based on the historic meta-data set and used to predict optimum k value for a new data set. Some exclusive experiments are conducted to verify the proposed method. The results shows that the local complexity features could reflect the inner structure of a data set which could help find the optimum k for k-NN for different domains. Show more

Keywords: k-NN classifier, data sets, local complexity profile, optimum k

DOI: 10.3233/JIFS-161062

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 1, pp. 55-65, 2017

Authors: Babu, S. | Ananthanarayanan, N.R.

Article Type: Research Article

Abstract: Research focus increases rapidly on recent years in mining imbalanced data set, because of its challenge and its extensive application on the real world. A dataset is said to be imbalance, if categories of the classification attribute is not evenly represented. A fine balanced dataset is an important source for the classifiers to define the best prediction model. All the existing classifiers are inclined to perform poor on the imbalanced datasets. The reason for this is, all the classifiers seek to optimize their overall accuracy not by considering the relative distribution of each class. Hence, it is very essential to …go for well balanced dataset for classification. In this paper, the comprehensive Enhanced Minority Oversampling TEchnique (EMOTE) is proposed to improve the performance of the classifier by balancing the dataset. The key idea of the proposed method is to balance the dataset by tuning the misclassified instances of the minority classes into correctly classified instances through oversampling their nearest neighbor. To investigate the performance of the proposed model, different oversampling and under sampling methods inclusive of the well known method SMOTE (Synthetic Minority Oversampling TEchnique) are considered. Various imbalanced datasets from the UCI machine learning repository are considered for experiments The experimental results shows that, the proposed method EMOTE outperformed the other methods in balancing the dataset. In addition to this it is also proved that, the classifier is able to effectively improve its performance on the dataset which is generated by EMOTE. Show more

Keywords: Imbalanced dataset, classification, nearest neighbor, oversampling, under sampling

DOI: 10.3233/JIFS-161114

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 1, pp. 67-78, 2017

Authors: Alves, Elton Rafael | Tavares da Costa Jr, Carlos | Lopes, Márcio Nirlando Gomes | da Rocha, Brígida Ramati Pereira | de Sá, José Alberto Silva

Article Type: Research Article

Abstract: Atmospheric discharges offer great risks to the population and activities that involve different systems such as telecommunications, energy distribution and transportation. Lightning prediction can contribute to minimize the risks of this natural phenomenon. Therefore the present paper presents a model for lightning prediction based on satellite atmospheric sounding data, calibrated and validated with lightning data in an Amazon region particular area through an investigation that considered five period cases for validation of lightning prediction: case 1 (one hour), case 2 (two hours), case 3 (three hours), case 4 (four hours) and case 5 (five hours). The machine learning technique used …to predict lightning was the Artificial Neural Network (ANN) trained with Levenberg-Marquardt backpropagation algorithm to classify modeling related to lightning prediction. This classification relied on the possibility of lightning prediction from the vertical profile of air temperature obtained from satellite NOAA-19. Results show that ANN was capable of identifying adequately the class to which a new event belongs to in relation to categories of occurrence and absence of lightning with better performance than traditional methodologies. Show more

Keywords: Classifiers, artificial neural network, prediction of atmospheric discharges, satellite atmospheric sounding

DOI: 10.3233/JIFS-161152

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 1, pp. 79-92, 2017

Authors: He, Xiaorong | Wu, Yingyu

Article Type: Research Article

Abstract: This paper aims to study an effective group decision making (GDM) method for dealing with the hesitant fuzzy context and apply it to solving person and post matching problem. We studied the hesitant fuzzy information aggregation problem in two folds, 1) consider confidence levels of the decision makers; 2) consider different priority levels of the decision makers or criteria. Firstly, we propose some Hamacher based aggregation operators considering different confidence levels and Hamacher based aggregation operator considering different prioritized levels. Then, special cases of these operators and numerical examples are analyzed. Finally, we propose a new GDM method using these …operators and applied to person and post matching problem. Show more

Keywords: Personnel selection, hesitant fuzzy set, multi-attribute GDM, aggregation operators

DOI: 10.3233/JIFS-161159

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 1, pp. 93-103, 2017

Authors: Ahmadzade, Hamed | Gao, Rong | Dehghan, Mohammad Hossein | Sheng, Yuhong

Article Type: Research Article

Abstract: In this paper, we mainly propose a definition of partial entropy for uncertain random variables. In fact, partial entropy is a tool to characterize how much of entropy of an uncertain random variable belongs to the uncertain variable. Furthermore, some properties of partial quadratic entropy are investigated such as positive linearity. Finally, some other types of partial entropies are studied.

Keywords: Chance theory, uncertain random variable, entropy, partial entropy

DOI: 10.3233/JIFS-161161

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 1, pp. 105-112, 2017

Authors: Li, Xianghui | Sun, Hao | Hou, Dongshuang

Article Type: Research Article

Abstract: Borm et al. (1992) characterized the position value for cooperative games with deterministic communication structure introduced by Myerson (1977). Now we follow the Myerson model to consider the uncertainty about the participation levels of players. In this setting, the position value defined in terms of its crisp version is associated with partitions by levels of players and different ways to obtain these partitions lead to different position values with particular forms. We provide several characterizations of the specific position values from the perspective of generalized component efficiency and balanced link contributions, link potential function, and effort function linked to the …Choquet partition and multilinear partition respectively. Show more

Keywords: Fuzzy cooperative game, communication structure, position value, fuzzy core

DOI: 10.3233/JIFS-16117

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 1, pp. 113-124, 2017