Journal of Intelligent & Fuzzy Systems - Volume 40, issue 1

Authors: Hu, Ziyu | Ma, Xuemin | Sun, Hao | Yang, Jingming | Zhao, Zhiwei

Article Type: Research Article

Abstract: When dealing with multi-objective optimization, the proportion of non-dominated solutions increase rapidly with the increase of optimization objective. Pareto-dominance-based algorithms suffer the low selection pressure towards the true Pareto front. Decomposition-based algorithms may fail to solve the problems with highly irregular Pareto front. Based on the analysis of the two selection mechanism, a dynamic reference-vector-based many-objective evolutionary algorithm(RMaEA) is proposed. Adaptive-adjusted reference vector is used to improve the distribution of the algorithm in global area, and the improved non-dominated relationship is used to improve the convergence in a certain local area. Compared with four state-of-art algorithms on DTLZ benchmark with …5-, 10- and 15-objective, the proposed algorithm obtains 13 minimum mean IGD values and 8 minimum standard deviations among 15 test problem. Show more

Keywords: Many-objective optimization, evolutionary algorithm, Gaussian mixture model, selection mechanism

DOI: 10.3233/JIFS-192124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 449-461, 2021

Authors: Li, Juan | Shao, Yabin | Qi, Xiaoding

Article Type: Research Article

Abstract:  With respect to multiple attribute group decision making problems in which the attribute weights and the expert weights take the form of real numbers and the attribute values take the form of interval-valued uncertain linguistic variable. In this paper, we introduce the idea of variable precision into the incomplete interval-valued fuzzy information system and propose the theory of variable precision rough sets over incomplete interval-valued fuzzy information systems. Then, we give the properties of rough approximation operators and study the knowledge discovery and attribute reduction in the incomplete interval-valued fuzzy information system under the condition that a certain degree of …misclassification rate is allowed to exist. Furthermore, a decision rule and decision model are given. Finally, an illustrative example is given and compared with the existing methods, the practicability and effectiveness of this method are further verified. Show more

Keywords: Interval-valued fuzzy set, incomplete information systems, variable precision interval-valued rough fuzzy set, attribute reduction, decision rules

DOI: 10.3233/JIFS-192161

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 463-475, 2021

Authors: Xu, Yanping | Ye, Tingcong | Wang, Xin | Lai, Yuping | Qiu, Jian | Zhang, Lingjun | Zhang, Xia

Article Type: Research Article

Abstract: In the field of security, the data labels are unknown or the labels are too expensive to label, so that clustering methods are used to detect the threat behavior contained in the big data. The most widely used probabilistic clustering model is Gaussian Mixture Models(GMM), which is flexible and powerful to apply prior knowledge for modelling the uncertainty of the data. Therefore, in this paper, we use GMM to build the threat behavior detection model. Commonly, Expectation Maximization (EM) and Variational Inference (VI) are used to estimate the optimal parameters of GMM. However, both EM and VI are quite sensitive …to the initial values of the parameters. Therefore, we propose to use Singular Value Decomposition (SVD) to initialize the parameters. Firstly, SVD is used to factorize the data set matrix to get the singular value matrix and singular matrices. Then we calculate the number of the components of GMM by the first two singular values in the singular value matrix and the dimension of the data. Next, other parameters of GMM, such as the mixing coefficients, the mean and the covariance, are calculated based on the number of the components. After that, the initialization values of the parameters are input into EM and VI to estimate the optimal parameters of GMM. The experiment results indicate that our proposed method performs well on the parameters initialization of GMM clustering using EM and VI for estimating parameters. Show more

Keywords: Network threat detection, gaussian mixture models, expectation maximization, variational inference, singular value decomposition, parameters initialization

DOI: 10.3233/JIFS-200066

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 477-490, 2021

Authors: Shen, Ao | Peng, Shuling | Liu, Gaofei

Article Type: Research Article

Abstract: The probabilistic linguistic term sets (PLTSs) are widely used in decision-making, due to its convenience of evaluation, and allowances of probability information. However, there are still some cases where it is not convenient to give an evaluation using the PLTS gramma. Sometimes the evaluators can only give a comparative relationship between alternatives, sometimes evaluators may have difficulty understanding all the alternatives and cannot give a complete assessment. Therefore, we propose a method to transform the comparative linguistic expressions (CLEs) into PLTSs, and the comparison objects of CLEs are alternatives evaluated by PLTSs. And the probability distribution has been adjusted to …make the transformation more in line with common sense. Then, a method to correct the deviation is proposed, allowing alternatives to be compared in the case of incomplete assessment. Combining the above two methods, we propose a decision-making method when both CLEs and incomplete assessments coexist. With the study in this paper, the limitations of PLTS-based evaluation and decision-making are reduced and the flexibility of using PLTS is improved. Show more

Keywords: Probabilistic linguistic term sets, comparative linguistic expressions, incomplete assessments, transforming, decision-making

DOI: 10.3233/JIFS-200103

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 491-506, 2021

Authors: Jiang, Jianming | Wu, Wen-Ze | Li, Qi | Zhang, Yu

Article Type: Research Article

Abstract: The hydropower plays a key role in electricity system owing to its renewability and largest share of clean electricity generation that promotes sustainable development of national economy. Developing a proper forecasting model for the quarterly hydropower generation is crucial for associated energy sectors, which could assist policymakers in adjusting corresponding schemes for facing with sustained demands. For this purpose, this paper presents a fractional nonlinear grey Bernoulli model (abbreviated as FANGBM(1,1)) coupled seasonal factor and Particular Swarm Optimization (PSO) algorithm, namely PSO algorithm-based FASNGBM(1,1) model. In the proposed method, the moving average method that eliminates the seasonal fluctuations is introduced …into FANGBM(1,1), then in which the structure parameters of FASNGBM(1,1) are determined by PSO. Based on hydropower generation of China from the first quarter of 2011 to the final quarter of 2018 (2011Q1-2018Q4), the numerical results show that the proposed model has a better performance than that of other benchmark models. Eventually, the quarterly hydropower generation of China from 2019 to 2020 are forecasted by the proposed model, according to results, the hydropower generation of China will reach 11287.14 × 108 Kwh in 2020. Show more

Keywords: Quarterly hydropower generation, seasonal fluctuation, FASNGBM(1,1), Particle Swarm Optimization (PSO)

DOI: 10.3233/JIFS-200113

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 507-519, 2021

Authors: Zhai, Junhai | Qi, Jiaxing | Zhang, Sufang

Article Type: Research Article

Abstract: The condensed nearest neighbor (CNN) is a pioneering instance selection algorithm for 1-nearest neighbor. Many variants of CNN for K -nearest neighbor have been proposed by different researchers. However, few studies were conducted on condensed fuzzy K -nearest neighbor. In this paper, we present a condensed fuzzy K -nearest neighbor (CFKNN) algorithm that starts from an initial instance set S and iteratively selects informative instances from training set T , moving them from T to S . Specifically, CFKNN consists of three steps. First, for each instance x  ∈ T , it finds the K -nearest neighbors in S …and calculates the fuzzy membership degrees of the K nearest neighbors using S rather than T . Second it computes the fuzzy membership degrees of x using the fuzzy K -nearest neighbor algorithm. Finally, it calculates the information entropy of x and selects an instance according to the calculated value. Extensive experiments on 11 datasets are conducted to compare CFKNN with four state-of-the-art algorithms (CNN, edited nearest neighbor (ENN), Tomeklinks, and OneSidedSelection) regarding the number of selected instances, the testing accuracy, and the compression ratio. The experimental results show that CFKNN provides excellent performance and outperforms the other four algorithms. Show more

Keywords: K-nearest neighbor, fuzzy K-nearest neighbor, fuzzy membership degree, instance selection, information entropy

DOI: 10.3233/JIFS-200124

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 521-533, 2021

Authors: Mandal, Ashis Kumar | Sen, Rikta | Chakraborty, Basabi

Article Type: Research Article

Abstract: The fundamental aim of feature selection is to reduce the dimensionality of data by removing irrelevant and redundant features. As finding out the best subset of features from all possible subsets is computationally expensive, especially for high dimensional data sets, meta-heuristic algorithms are often used as a promising method for addressing the task. In this paper, a variant of recent meta-heuristic approach Owl Search Optimization algorithm (OSA) has been proposed for solving the feature selection problem within a wrapper-based framework. Several strategies are incorporated with an aim to strengthen BOSA (binary version of OSA) in searching the global best solution. …The meta-parameter of BOSA is initialized dynamically and then adjusted using a self-adaptive mechanism during the search process. Besides, elitism and mutation operations are combined with BOSA to control the exploitation and exploration better. This improved BOSA is named in this paper as Modified Binary Owl Search Algorithm (MBOSA). Decision Tree (DT) classifier is used for wrapper based fitness function, and the final classification performance of the selected feature subset is evaluated by Support Vector Machine (SVM) classifier. Simulation experiments are conducted on twenty well-known benchmark datasets from UCI for the evaluation of the proposed algorithm, and the results are reported based on classification accuracy, the number of selected features, and execution time. In addition, BOSA along with three common meta-heuristic algorithms Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), and Binary Genetic Algorithm (BGA) are used for comparison. Simulation results show that the proposed approach outperforms similar methods by reducing the number of features significantly while maintaining a comparable level of classification accuracy. Show more

Keywords: Feature subset selection, binary owl search algorithm, meta-heuristic, optimization, self adaptive mechanism

DOI: 10.3233/JIFS-200258

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 535-550, 2021

Authors: Lu, Liqiong | Wu, Dong | Tang, Ziwei | Yi, Yaohua | Huang, Faliang

Article Type: Research Article

Abstract: This paper focuses on script identification in natural scene images. Traditional CNNs (Convolution Neural Networks) cannot solve this problem perfectly for two reasons: one is the arbitrary aspect ratios of scene images which bring much difficulty to traditional CNNs with a fixed size image as the input. And the other is that some scripts with minor differences are easily confused because they share a subset of characters with the same shapes. We propose a novel approach combing Score CNN, Attention CNN and patches. Attention CNN is utilized to determine whether a patch is a discriminative patch and calculate the contribution …weight of the discriminative patch to script identification of the whole image. Score CNN uses a discriminative patch as input and predict the score of each script type. Firstly patches with the same size are extracted from the scene images. Secondly these patches are used as inputs to Score CNN and Attention CNN to train two patch-level classifiers. Finally, the results of multiple discriminative patches extracted from the same image via the above two classifiers are fused to obtain the script type of this image. Using patches with the same size as inputs to CNN can avoid the problems caused by arbitrary aspect ratios of scene images. The trained classifiers can mine discriminative patches to accurately identify some confusing scripts. The experimental results show the good performance of our approach on four public datasets. Show more

Keywords: Script identification, score CNN, attention CNN, discriminative patches, scene images

DOI: 10.3233/JIFS-200260

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 551-563, 2021

Authors: Zhang, Di | Li, Pi-Yu | An, Shuang

Article Type: Research Article

Abstract: In this paper, we propose a new hybrid model called N -soft rough sets, which can be seen as a combination of rough sets and N -soft sets. Moreover, approximation operators and some useful properties with respect to N -soft rough approximation space are introduced. Furthermore, we propose decision making procedures for N -soft rough sets, the approximation sets are utilized to handle problems involving multi-criteria decision-making(MCDM), aiming at electing the optional objects and the possible optional objects based on their attribute set. The algorithm addresses some limitations of the extended rough sets models in dealing with inconsistent decision problems. …Finally, an application of N -soft rough sets in multi-criteria decision making is illustrated with a real life example. Show more

Keywords: Rough sets, N-soft sets, N-soft rough sets, Decision making analysis

DOI: 10.3233/JIFS-200338

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 565-573, 2021

Authors: Manickavasagam, B. | Amutha, B. | Revathi, M. | Karthick, N. | Sree Kumar, K. | Priyanka, K.

Article Type: Research Article

Abstract: Wireless Sensor Node (WSN) helps to track inpatient and remote patient (home/working) health information. Mishandling of the electronic system, patient behaviour and environmental changes which are all lead to incorrect data generation while using WSN for medical purposes. It leads to a false alarm being raised, network resource wastage, a false node priority level and low reliability. We have introduced the Mutual Trust Model (MTM) for Wireless Body Area Network (WBAN) with the help of Fog-Node (FN) to address these issues and to ensure the trustworthiness of the information acquired. In this, First-Hand Trust Method calculates the confidence value of …the individual sensor node. Then, with neighbor node support, the Stigmercy Trust Method (STM) is implemented to reinforce the trust source node. Ultimately, the individual patient’s confidence value for the MTM model is determined. With the assistance of the wireless-mininet network emulator and the RYU controller, the network environment model implement, and the results have been obtained. MTM predicts the confidence level of the collected data significantly and produces an accuracy of 92.3 percentage to prevent the emergency band from being used dispensable. Show more

Keywords: Trust analysis, WBAN, data reliability, direct and indirect trust method, and relative trust approach

DOI: 10.3233/JIFS-200363

Citation: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 1, pp. 575-589, 2021