Journal of Intelligent & Fuzzy Systems - Volume 33, issue 4

Authors: Wang, Chang

Article Type: Research Article

Abstract: In this paper, we first define vague parameterized vague soft sets (vpvs-sets) and study some of their properties. We then introduce vpvs-aggregation operator to form vpvs-decision making method that allows constructing more efficient decision processes. Finally, we give a numerical example to show the method working successfully for problems containing uncertain data.

Keywords: vague set, soft set, vague soft set, vague parameterized vague soft set, decision making

DOI: 10.3233/JIFS-17423

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 4, pp. 2341-2350, 2017

Authors: Ke, Hua | Zhao, Chenkai

Article Type: Research Article

Abstract: Resource leveling problem is to make a schedule for the minimization of resource fluctuation subject to precedence constraint and other specific constraints. When indeterminacies come into play, the leveled baseline schedule obtained by solving deterministic resource leveling problem can hardly be executed as planned and this schedule may even become infeasible. In this paper, on the basis of uncertainty theory, we consider an uncertain resource leveling problem in which activity durations are estimated by experts. In order to deal with these estimations, three uncertainty-theory-based project scheduling models are proposed and we utilize revised estimation of distribution algorithms to search quasi-optimal …schedules. Numerical experiments are also provided to illustrate the effectiveness of the algorithms. Show more

Keywords: Project scheduling, uncertainty theory, resource leveling, estimation of distribution algorithm

DOI: 10.3233/JIFS-17493

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 4, pp. 2351-2361, 2017

Authors: Konwar, Nabanita | Debnath, Pradip

Article Type: Research Article

Abstract: In classical functional analysis, continuous mappings are essential for the study of many important theorems such as open mapping theorem, closed graph theorem and uniform boundedness theorem. These results are yet to be established in the general setting of an intuitionistic fuzzy n -normed linear space (IFnNLS). This motivates us to introduce the notion of continuous linear operators in this generalized setting. Furthermore, we establish the uniform continuity theorem and Banach’s contraction principle in an IFnNLS.

Keywords: Intuitionistic fuzzy n-normed linear space, intuitionistic fuzzy continuous linear operator, Banach contraction principle

DOI: 10.3233/JIFS-17500

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 4, pp. 2363-2373, 2017

Authors: Huang, Yan | Wang, Ying-Ming

Article Type: Research Article

Abstract: Efficiency assessment by using data envelopment analysis (DEA) in interval environment is studied. Two parameters with regard to the input and output are introduced to characterize the variability of the production possibility sets. The extended production facets with different production possibility sets are determined. The inclusion relation between different extended production facets is discussed, and self-evaluation models are constructed to calculate the interval efficiency of the decision-making units (DMUs) with the optimal production facet. By setting self-evaluation as a target, the aggressive and benevolent cross-efficiency models are established based on the likelihood between the values of self-evaluation and peer evaluation. …The analysis of the models yields the interval cross-efficiency matrices and the weight allocation method that is more advantageous to the DMU for aggregating the interval cross-efficiency matrices. An example is used to illustrate the applications of the models. Show more

Keywords: Interval programming, Pareto optimal, DEA, cross-efficiency

DOI: 10.3233/JIFS-17514

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 4, pp. 2375-2389, 2017

Authors: Zhu, Kuan Yun | Hu, Bao Qing

Article Type: Research Article

Abstract: In this paper, we investigate the relationship among soft sets, rough sets, fuzzy sets and lattices. The notion of soft rough fuzzy lattices (ideals, filters) over lattices is introduced, which is an extended notion of soft rough lattices (ideals, filters) and rough fuzzy lattices (ideals, filters) over lattices. Moreover, we study roughness in lattices with respects to a soft approximation space. Some new soft rough fuzzy operations over lattices are explored. In particular, lower and upper soft rough fuzzy lattices (ideals, filters) over lattices with respect to another fuzzy soft set are investigated.

Keywords: Soft rough set, fuzzy sublattice (ideal, filter), soft rough fuzzy lattice (ideal, filter)

DOI: 10.3233/JIFS-17520

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 4, pp. 2391-2402, 2017

Authors: Łuczak, Maciej

Article Type: Research Article

Abstract: The dynamic time warping (DTW) distance measure is one of the popular and efficient distance measures used in algorithms of time series classification. It frequently occurs with different kinds of transformations of input data. In this paper we propose a combination of the DTW distance measure with a (discrete) integral transformation. This means that the new distance measure IDTW is simply calculated as the value of DTW on the integrated input time series. However, this design means that the distance cannot in itself give good classification results. We therefore propose to construct a parametric integral dynamic time warping distance measure …IDDTW which is a parametric combination of the distances DTW and IDTW. Such a combined distance is used in the nearest neighbor (1NN) classification method in the case of both univariate and multivariate time series analysis. Computational experiments performed on both one-dimensional and multidimensional datasets show that this approach reduces the classification error significantly in comparison with the component methods. The parametric approach allows the new distance to be adapted to each dataset, while showing no significant overfitting effects. The contribution and the main motivation of the paper is to show that the simple transformation as the integral transform can include a bit information about examined time series data and can be used to significantly improve performance of the classification process both for univariate and multivariate time series data. The results are confirmed by graphical and statistical comparisons. Show more

Keywords: Time series classification, univariate and multivariate time series data, dynamic time warping, parametric integral distance measure

DOI: 10.3233/JIFS-17523

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 4, pp. 2403-2413, 2017

Authors: Zhang, Shuanghong | Zhang, Qingling | Qiao, Liang | Ren, Junchao | Liu, Chao | Feng, Yifu

Article Type: Research Article

Abstract: In this paper, the optimal guaranteed cost control problem of a single species model with stage structure is studied by fuzzy methods. The non-linear harvesting model in a toxic environment is established, and the toxin considered has the characteristics of growth and reproduction. Furthermore, for the reason of practical problems, in which the period of production is a finite time and the biomass and toxin should be controlled into some range, the guaranteed cost controller is designed based on fuzzy models. While controlling the biomass concentration achieved the specified range, the controller in this paper eliminated the toxin entirely or …controlled the toxin in traces concentration according the actual demand of different environment. Finally, a practical example is carried out to illustrate the feasibility of designed controller in the case of non-toxin and traces of toxin, respectively. Show more

Keywords: Toxin, stage-structure, finite-time bounded, Takagi-Sugeno(T-S) fuzzy model, guaranteed cost control

DOI: 10.3233/JIFS-17556

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 4, pp. 2415-2426, 2017

Authors: Cai, Fei | Chen, Wanyu | Ou, Xinliang

Article Type: Research Article

Abstract: Query completion approaches assist searchers in formulating queries with few keystrokes when using an information retrieval system to address their information needs, which help users benefit from avoiding spelling mistakes and from producing clear query formulations, etc. Previous work on query completion algorithms returns a ranked list of queries to the users mostly based on the overall observed search popularity of query candidates in the whole query logs. However, the query search popularity could be changed over time, i.e., it’s time-aware. Thus, these ranking approaches based on the overall search popularity could not work very well and users may fail …to find an acceptable query in the returned list, resulting in a limited search satisfaction. Hence, this paper proposes a Learning-based Personalized Query Ranking approach, i.e., LQR, where the features on the observed and predicted search popularity both in the whole logs and the recent period are exploited. Taking a pair-wise learning scenario, this paper presents a method for generating a ranked list of query candidates, and then reranks the candidates by the similarity to current search context. The experimental results show the proposed approach outperforms the baseline in terms of Mean Reciprocal Rank (MRR), reporting an average MRR improvement of 7% against the baseline. Show more

Keywords: Information retrieval, query completion, query suggestion, query formulation

DOI: 10.3233/JIFS-17565

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 4, pp. 2427-2435, 2017

Authors: Huang, Chao | Liu, Mengying | Gong, Huiqun | Xu, Feifei

Article Type: Research Article

Abstract: Attraction recommendation plays an essential role in tourism. For example, it can relieve information overload for tourists and increase sales for tourism operators. When making travel decisions, tourists depend heavily on the personal preferences and suggestions from people they trust. However, most existing attraction recommendation methods focus on the tourist preferences for topics of attractions, yet overlook the seasonality in topic preferences. Additionally, extant studies are generally based on a single type of trust, which may represent trust relations inaccurately. In order to overcome these issues, we propose a novel season-aware attraction recommendation method based on the seasonal topic preferences …and dual-trust relations. Firstly, we capture tourists’ seasonal topic preferences by analyzing their travel histories along two dimensions: time and attraction. Secondly, we develop a dual-trust relationship (DTR) model based on familiarity-based trust and similarity-based trust, in contrast to existing studies that purely focus on a single type of trust. Thirdly, we propose a novel season-aware attraction recommendation method named SAR-DTR. In a specific season, it predicts ratings based on both topic preferences in the given season and suggestions from tourists they trust. To demonstrate the superiority of the proposed method to other approaches, an empirical study with real-world data was conducted. The experimental results regarding both prediction and recommendation performance are reported. Show more

Keywords: Attraction recommendation, seasonal topic preference, similarity-based trust, familiarity-based trust

DOI: 10.3233/JIFS-17569

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 4, pp. 2437-2449, 2017

Authors: Ahmad, Amir

Article Type: Research Article

Abstract: Different measures have been proposed to study brands. In this paper, it is studied whether regression methods can capture the relationship between customer satisfaction and brand measures. It is also investigated whether a combination of these brand measures is useful for the prediction of customer satisfaction. Various regression methods were employed and it was found that generally there was a high correlation (>0.7) between the combination of brand measures and customer satisfaction. Attribute selection methods were used to find out the most important components among all the components of different brand measures. Results suggest that a small subset of all …the components (7 out of 111) gives almost the same prediction accuracy as with all the components of different brand measures. This subset of components consists of components from different brand measures. The results emphasize that various brand measures should be combined to improve the prediction accuracy of customer satisfaction. Experiments also suggest that while various regression methods produce good results, support vector machine regression method generally perform best for this problem. Show more

Keywords: Brand measures, customer satisfaction, regression methods, correlation

DOI: 10.3233/JIFS-17573

Citation: Journal of Intelligent & Fuzzy Systems, vol. 33, no. 4, pp. 2451-2462, 2017