Optimal Rank Order Research Articles

Combining deep neural network and bibliometric indicator for emerging research topic prediction

Predicting emerging research topics is important to researchers and policymakers. In this study, we propose a two-step solution to the problem of emerging topic prediction. The first step forecasts the future popularity score, a novel indicator reflecting the impact and growth, of candidate topics in a time-series manner. The second step selects novel topics from the candidates predicted to be popular in the first step. Terms with domain characteristics are used as candidate topics. Deep neural networks, specifically LSTM and NNAR, are applied with nine features of topics to predict popularity score. We evaluated the models and five baselines on two datasets from two perspectives, i.e., the ability to (1) predict the correct indicator value and (2) reconstruct the optimal ranking order. Two types of training strategies were compared, including a global strategy that trains a model with all topics and two local strategies that train separate models with different groups of topics. Our results show that LSTM and NNAR outperform other models in predicting the value of popularity score measured by MAE and RMSE, while LightGBM is a competitive baseline in ranking the topics in terms of NDCG@20. The performance difference of global and local strategies is not significant. Emerging topics predicted by our approach are compared with those by other methods. A qualitative assessment on nominated emerging topics suggests topics nominated by machine learning methods are more alike than those by the rule-based model. Some important topics are nominated according to a preliminary literature analysis. This study exploited the strengths of both machine learning and bibliometric indicator approaches for emerging topic prediction. Deep neural networks are applied where objective optimization target can be defined and measured. Bibliometric indicator offers an efficient way to select novel topics from candidates. The hybrid approach shows promise in considering various characteristics of emerging topics when making predictions.

A simulated annealing-based permutation method and experimental analysis for multiple criteria decision analysis with interval type-2 fuzzy sets

The aim of this paper is to develop a simulated annealing-based permutation method for multiple criteria decision analysis within the environment of interval type-2 fuzzy sets. The outranking methodology constitutes one of the most fruitful approaches in multiple criteria decision making and has been applied in numerous real-world problems. The permutation method is a classical outranking model, which generalizes Jacquet–Lagreze's permutation method and is based on a pairwise criterion comparison of the alternatives. Because modeling of the uncertainty in the decision-making process becomes increasingly important, an extension to the interval type-2 fuzzy environment is a useful generalization of the permutation method and is appropriate for handling uncertain and imprecise information in practical decision-making situations. This paper produces a signed-distance-based comparison among the comprehensive rankings of alternatives for concordance and discordance analyses. An integrated nonlinear programming model is constructed for estimation of the criterion weights and the optimal ranking order of the alternatives under incomplete preference information. To enhance the implementation efficiency, a simulated annealing-based permutation method and its meta-heuristic algorithm are developed to produce a polynomial time solution for the total completion time problem. Furthermore, computational experiments with notably large amounts of simulation data are conducted to test the solution approach and validate the correctness of the approximate solution compared with the optimal all-permutation-based result.

AN INCLUSION COMPARISON APPROACH FOR MULTIPLE CRITERIA DECISION ANALYSIS BASED ON INTERVAL-VALUED INTUITIONISTIC FUZZY SETS

The theory of interval-valued intuitionistic fuzzy sets provides an intuitive and feasible way of addressing uncertain and ambiguous properties. Many useful models and methods have been developed for multiple criteria decision analysis within the interval-valued intuitionistic fuzzy environment. In contrast to the elaborate existing methods, this paper establishes a simple and effective method for managing the sophisticated data expressed by interval-valued intuitionistic fuzzy sets. An inclusion comparison possibility defined on interval-valued intuitionistic fuzzy sets is proposed, and some important properties are investigated. Then, an inclusion-based index that considers positive and negative ideals is offered. Considering the maximal comprehensive inclusion-based indices, this paper constructs a linear programming model (for consistent information) and an integrated, nonlinear programming model (for inconsistent information) to estimate the criterion weights and the optimal ranking order of the alternatives under an incomplete preference structure. The feasibility of the proposed method is illustrated by a practical example of selecting a suitable bridge construction method, and a comparative analysis with other relevant methods is conducted to validate the effectiveness and applicability of the proposed methodology.

A likelihood-based QUALIFLEX method with interval type-2 fuzzy sets for multiple criteria decision analysis

The aim of this paper is to develop a likelihood-based QUALIFLEX method with interval type-2 fuzzy sets for multiple criteria decision analysis. This paper considers the context of interval type-2 trapezoidal fuzzy numbers and also introduces an extended concept of likelihoods of interval type-2 fuzzy preference relations. The concepts of lower and upper likelihoods are presented to serve as lower and upper bounds, respectively, of the possibility of preference relations and then to determine the likelihoods between interval type-2 trapezoidal fuzzy numbers. This paper uses a likelihood-based comparison between evaluative ratings to propose the concepts of concordance indices and comprehensive concordance indices for evaluating the permutations of alternatives corresponding to criterion-by-criterion consistency. By employing the signed distance-based method or the optimal membership-degree-determination method, the best permutation and its corresponding optimal ranking order of the alternatives can be acquired through a comparison of all of the comprehensive concordance indices. The applicability and effectiveness of the proposed method are illustrated with a medical decision-making problem that addresses the selection of treatment options. Additionally, a comparative analysis is performed with some relevant multiple criteria decision-making methods to demonstrate the advantages of the likelihood-based QUALIFLEX method. Finally, an analysis through computational experiments is conducted to examine the implementation efficiency of the proposed method.

NONLINEAR ASSIGNMENT-BASED METHODS FOR INTERVAL-VALUED INTUITIONISTIC FUZZY MULTI-CRITERIA DECISION ANALYSIS WITH INCOMPLETE PREFERENCE INFORMATION

In the context of interval-valued intuitionistic fuzzy sets, this paper develops nonlinear assignment-based methods to manage imprecise and uncertain subjective ratings under incomplete preference structures and thereby determines the optimal ranking order of the alternatives for multiple criteria decision analysis. By comparing each interval-valued intuitionistic fuzzy number's score function, accuracy function, membership uncertainty index, and hesitation uncertainty index, a ranking procedure is employed to identify criterion-wise preference of alternatives. Based on the criterion-wise rankings and a set of known but incomplete information about criterion weights, a nonlinear assignment model is constructed to estimate criterion weights and to order the priority of various alternatives. Considering multiple criteria evaluation problems with preference conflict about criterion importance, an integrated nonlinear programming model is further established with regard to incomplete and inconsistent weight information. These proposed nonlinear assignment-based methods can obtain an aggregate ranking that effectively combines the relative performance of each alternative in each criterion. In addition, this overall ranking most closely agrees with the criterion-wise rankings. Finally, the feasibility of the proposed method is illustrated by a practical example of selecting a suitable bridge construction method.

Interval-valued fuzzy permutation method and experimental analysis on cardinal and ordinal evaluations

This paper presents interval-valued fuzzy permutation (IVFP) methods for solving multiattribute decision making problems based on interval-valued fuzzy sets. First, we evaluate alternatives according to the achievement levels of attributes, which admits cardinal or ordinal representation. The relative importance of each attribute can also be measured by interval or scalar data. Next, we identify the concordance, midrange concordance, weak concordance, discordance, midrange discordance and weak discordance sets for each ordering. The proposed method consists of testing each possible ranking of the alternatives against all others. The evaluation value of each permutation can be computed either by cardinal weights or by solving programming problems. Then, we choose the permutation with the maximum evaluation value, and the optimal ranking order of alternatives can be obtained. An experimental analysis of IVFP rankings given cardinal and ordinal evaluations is conducted with discussions on consistency rates, contradiction rates, inversion rates, and average Spearman correlation coefficients.

Suspense: Dynamic Incentives in Sports Contests

In a dynamic model of sports competition, if spectators care only about contestants’ efforts, incentive schemes depending linearly on the final score difference dominate rank order schemes based only on who wins. If spectators also care about suspense, defined as valuing more contestants’ efforts when the game is closer, rank order schemes can dominate linear score difference schemes, and this will be the case when the demand for suspense is sufficiently high. Under additional assumptions, we show that the optimal rank order scheme dominates a broad class of incentive schemes.

A unified design method for rank order, stack, and generalized stack filters based on classical Bayes decision

The authors present a unified method for designing optimal rank order filters (ROFs), stack filters, and generalized stack filters (GSFs) under the mean absolute error (MAE) criterion. The method is based on classical Bayes minimum-cost decision. Both the a priori and the a posteriori approaches are considered. It is shown that designing the minimum MAE stack filters and GSFs is equivalent to the a priori Bayes decision. The authors develop a suboptimal routine which requires no linear program (LP) for finding reasonably good filters. They determine sufficient conditions under which the suboptimal routine produces optimal solutions. The procedure is developed separately for ROFs, stack filters, and GSFs. Some design examples are presented, and the application of stack filters and GSFs to image recovery from impulsive noise is considered. It is shown how the complexity of finding optimal stack filters and GSFs can be reduced.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Asymptotically Optimal Rank Order Tests For Progressive Censoring*

Asymptotically Optimal Rank Order Tests For Progressive Censoring^*

A program to determine the optimal rank order of pairwise similarities from incomplete triads data: Profit

Description. Nonmetric scaling techniques (Kruskal, 1964; Shepard, 1962a, b; Shepard, 1966) as embodied in the TORSCA (Young & Torgerson, 1967) or PARSCAL (Johnson, 1973) programs produce multidimensional scaling solutions from rank orders of pairwise similarities between N points. The present program produces a rank order for (~) pairs of stimuli for data gathered by Torgerson's method of triadic combinations (Torgerson, 1958) or, more· commonly, the method of incomplete triads. Coombs (1964, pp. 352-359) suggests a method of triangular analysis to arrange these triad judgments inte a single rank order. We have supplemented Coombs' basic algorithm with a set of routines to transform triangular matrices to poduce the best rank orders in terms of the fewest number of inconsistencies. With sophisticated subjects, the rank orders needed for the nonmetric scaling programs can be obtained directly by asking the subject to rank order all pairs of stimuli in order of similarity. The method of incomplete triads is particularly appropriate for subjects who cannot consider a large number of relationships simultaneously but who can decide which two of three stimuli are most similar and which two are least similar. Children as young as 5 years of age can make such judgments. Seitz (1971) demonstrated the usefulness of this type of data, using metric scaling techniques suggested by Torgerson (1958). Metric techniques, however, require repeated measurement on each triad since distance is estimated from frequency of choice. For more than five stimuli, many trials for a single subject or the data from several subjects must be combined to obtain the necessary measures. There is a major advantage in not combining the data from more than one subject since one is usually interested in a description of the perceived similarity among stimuli for individuals. The nonmetric procedures used in the present program can be employed without repeated measurements on each triad and can be applied to individual subjects. Thus, for six stimuli, only 20 trials are necessary; for nine stimuli, 84 trials are necessary. Input. Input consists of the data gathered from all possible triads, given N stimuli. The data from each triadic stimulus array forms a data input unit consisting of the designation numbers of the two stimuli judged as most similar and the two stimuli judged as least similar. The data input units can be read into the program in any order as long as the data within each unit follows the sequence: most similar pair, least similar pair.