Existing Ranking Approaches Research Articles

The novel approach for ranking generalized interval type-2 trapezoidal fuzzy numbers based on integral value

Interval type 2 fuzzy sets (IT2FS) give us with additional value of membership function to represent the uncertainty and the fuzziness of the real word than type 1 fuzzy sets (T1FS). In real problem, the ranking of interval type 2 fuzzy numbers is an important component in the fuzzy decision making system. The existing ranking approaches i.e. centroid is cited by many researchers and has wide application, and then integral value also often developed by researchers both in the form of T1FS and newly in the form IT2FS. However, the existing ranking approaches still have shortcomings of the ranking function. In this paper, the concepts of left and right of interval type-2 trapezoidal fuzzy numbers (IT2TrFN) i.e the novel total integral value with index of optimistic is proposed to overcome the shortcomings of the existing ranking approaches. By using several examples, the new total integral value is capable to cover the shortcomings of the existing approaches and has well-consistency in ranking IT2TrFN with equal and different hight value.

Relative-distance-based approaches for ranking intuitionistic fuzzy values

During the uncertain information processing on Atanassov’s intuitionistic fuzzy sets, the ranking for intuitionistic fuzzy values (IFVs) is an important and omnipresent issue. Even though many orders used to compare any two IFVs have been proposed, some shortcomings, such as inadmissibility, nonrobustness, and nondeterminacy, may exist when these orders are utilized. Inspired by the Euclidean approach for ranking IFVs, we present a novel order that can overcome the aforementioned shortcomings using the notion of relative geometric distance. With the help of graphic representation of an IFV, we analyze the existing popular approaches for ranking IFVs and point out their drawbacks. Taking into account these three distances between an IFV and the ideal negative point, ideal positive point and most uncertain point, respectively, we present a relative-distance-based mensuration for describing the favorable degree of the IFV. Accordingly, the boundaries used in the existing ranking approaches for IFVs are replaced by a novel curve. We prove that the proposed method satisfies the admissibility, robustness, and determinacy requirements. Finally, we extend the presented ranking method for IFVs by introducing human attitudes and compare the proposed approach with the existing ones, which indicates its availability and rationality. We then can obtain the conclusion that the relative distance is an effective and reasonable approach for ranking IFVs.

Integrated data envelopment analysis and multicriteria decision-making ranking approach based on peer-evaluations and subjective preferences: case study in banking sector

PurposeThis paper is focused on developing an integrated algorithmic approach named as data envelopment analysis and multicriteria decision-making (DEA-MCDM) for ranking decision-making units (DMUs) based on cross-efficiency technique and subjective preference(s) of the decision maker.Design/methodology/approachSelf-evaluation in data envelopment analysis (DEA) lacks in discrimination power among DMUs. To fix this, a cross-efficiency technique has been introduced that ranks DMUs based on peer-evaluation. Different cross-efficiency formulations such as aggressive and benevolent and neutral are available in the literature. The existing ranking approaches fail to incorporate subjective preference of “one” or “some” or “all” or “most” of the cross-efficiency evaluation formulations. Therefore, the integrated framework in this paper, based on DEA and multicriteria decision-making (MCDM), aims to present a ranking approach to incorporate different cross-efficiency formulations as well as subjective preference(s) of decision maker.FindingsThe proposed approach has an advantage that each of the aggressive, benevolent and neutral cross-efficiency formulations contribute to select the best alternative among the DMUs in a MCDM problem. Ordered weighted averaging (OWA) aggregation is applied to aggregate final cross-efficiencies and to achieve complete ranking of the DMUs. This new approach is further illustrated and compared with existing MCDM approaches like simple additive weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prove its validity in real situations.Research limitations/implicationsThe choice of cross-efficiency formulation(s) as per subjective preference of the decision maker and different orness levels lead to different aggregated scores and thus ranking of the DMUs accordingly. The proposed ranking approach is highly useful in real applications like R and D projects, flexible manufacturing systems, electricity distribution sector, banking industry, labor assignment and the economic environmental performances for ranking and benchmarking.Practical implicationsTo prove the practical applicability and robustness of the proposed integrated DEA-MCDM approach, it is applied to top twelve Indian banks in terms of three inputs and two outputs for the period 2018–2019. The findings of the study (1) ensure the impact of non-performing assets (NPAs) on the ranking of the selected banks and (2) are enormously valuable for the bank experts and policy makers to consider the impact of peer-evaluation and subjective preference(s) in formulating appropriate policies to improve performance and ranks of underperformed banks in competitive scenario.Originality/valueTo the best of the authors’ knowledge, this is the first study that has integrated both DEA and MCDM via OWA aggregation to present a ranking approach that can incorporate different cross-efficiency formulations and subjective preference(s) of the decision maker for ranking DMUs.

Novel Multiple Attribute Group Decision-Making Methods Based on Linguistic Intuitionistic Fuzzy Information

As an effective technique to qualitatively depict assessment information, a linguistic intuitionistic fuzzy number (LIFN) is more appropriate to portray vagueness and indeterminacy in actual situations than intuitionistic fuzzy number (IFN). The prominent feature of a Muirhead mean (MM) operator is that it has the powerful ability to capture the correlations between any input-data and MM operator covers other common operators by assigning the different parameter vectors. In the article, we first analyze the limitations of the existing ranking approaches of LIFN and propose a novel ranking approach to surmount these limitations. Secondly, we propound several novel MM operators to fuse linguistic intuitionistic fuzzy (LIF) information, such as the LIF Muirhead mean (LIFMM) operator, the weighted LIF Muirhead mean (WLIFMM) operator and their dual operators, the LIFDMM operator and the WLIFDMM operator. Subsequently, we discuss several desirable properties along with exceptional cases of them. Moreover, two novel multiple attribute group decision-making approaches are developed based upon these operators. Ultimately, the effectuality and practicability of the propounded methods are validated through dealing with a global supplier selection issue, and the comparative analysis and the merits of the presented approaches are demonstrated by comparing them with existing approaches.

Momentum and the Cross-Section of Stock Volatility

Recent literature shows that strategies exhibit significant downside risks over certain periods, or called momentum crashes. We find that the high uncertainty of strategies is sourced from the cross-sectional volatility of individual stocks. Stocks with high realised volatility over the formation period tend to lose effect, while stocks with low realised volatility show strong momentum. A new approach, generalised risk-adjusted (GRJMOM), is introduced to mitigate the negative impact of high risks. GRJMOM is proven to be more profitable and less risky than the existing ranking approaches in multiple asset classes, including the UK stock, commodity, global equity index, and fixed income markets.

Can online user reviews be more helpful? Evaluating and improving ranking approaches

Given the sharply increasing number of online reviews, the selection of strategies by review-hosting firms to help users access more helpful reviews is an intriguing but insufficiently studied issue. We first propose a model to help us understand how reviews receive helpful votes (HV) and non-helpful votes. According to this model, the performances of different ranking approaches are compared using several simulated datasets with empirical features. In addition to three well-known ranking approaches, we develop a novel approach based on Bayesian statistics that is easy to implement in existing websites and can be combined with other content recommendation techniques to determine the prior belief in online reviews. More importantly, we suggest two simple ways to enhance existing ranking approaches. The numerical evidence demonstrates the advantages of two enhanced approaches, as indicated by higher helpful ratios and a reduced Matthew effect. These findings have important practical implications for consumers, online retailers, and review-hosting firms.

Novel ranking method of interval numbers based on the Boolean matrix

In this paper, we first analyze several existing ranking approaches for a set of interval numbers which are based on the possibility degree matrix. We show by counterexamples that these methods are not appropriate to rank a set of interval numbers from two viewpoints. Furthermore, we investigate the relationship between the possibility degree matrix and the reciprocal fuzzy preference matrix and analyze the reason that leads to the irrational ranking result. Finally, we propose an improved ranking algorithm for a set of interval numbers based on the Boolean matrix. Two numerical examples and a practical application are provided to illustrate the feasibility and effectiveness of our proposed method.

Semantic relation based personalized ranking approach for engineering document retrieval

Since engineering design is heavily informational, engineers want to retrieve existing engineering documents accurately during the product development process. However, engineers have difficulties searching for documents because of low retrieval accuracy. One of the reasons for this is the limitation of existing document ranking approaches, in which relationships between terms in documents are not considered to assess the relevance of the retrieved documents. Therefore, we propose a new ranking approach that provides more correct evaluation of document relevance to a given query. Our approach exploits domain ontology to consider relationships among terms in the relevance scoring process. Based on domain ontology, the semantics of a document are represented by a graph (called Document Semantic Network) and, then, proposed relation-based weighting schemes are used to evaluate the graph to calculate the document relevance score. In our ranking approach, user interests and searching intent are also considered in order to provide personalized services. The experimental results show that the proposed approach outperforms existing ranking approaches. A precisely represented semantics of a document as a graph and multiple relation-based weighting schemes are important factors underlying the notable improvement.

A novel method for ranking of vague sets for handling the risk analysis of compressor system

Vague sets were first proposed by Gau and Buehrer [11] as an extension of fuzzy sets which encompass fuzzy sets, inter-valued fuzzy sets, as special cases. Vague sets consist of two parts, that is, the membership function and nonmembership function. Therefore, in accordance with practical demand these sets are more flexible than the existing fuzzy sets and provide much more information about the situation. In this paper, a new approach for the ranking of trapezoidal vague sets is introduced. Shortcomings in some existing ranking approaches have been pointed out. Validation of the proposed ranking method has been established through the reasonable properties of the fuzzy quantities. Further, the proposed ranking approach is applied to develop a new method for dealing with vague risk analysis problems to find the probability of failure, of each component of compressor system, which could be used for managerial decision making and future system maintenance strategy. Also, the proposed method provides a useful way for handling vague risk analysis problems.

Comparison Of Fuzzy Numbers With Ranking Fuzzy And Real Number

Ranking fuzzy numbers play as a key tool in many applied models in the world and in particular decision-making procedures. We are going to present a new method based on the ranking the fuzzy number and real number. The problem of ranking the fuzzy number and real number is proposed with ranking function and then this approach to extend the ranking of two fuzzy numbers with ranking function. The proposed method is illustrated by some numerical examples and in particular the results of ranking by the proposed method and some common and existing methods for ranking fuzzy sets is compared to verify the advantage of the new approach. We will see that against of most existing ranking approaches where for two fuzzy sets are the exact ranking, the above men sioned method can give a ranking fuzzy numbers with acceptance rate smaller as fuzzy.

Ranking of Exponential Vague Sets With an Application to Decision Making Problems

The main aim of this paper is to propose a new approach for the ranking of exponential vague sets. The concepts ofexponential vague sets and arithmetic operations between two exponential vague sets are introduced. Theshortcomings of some existing ranking approaches for the ranking of generalized fuzzy sets and intuitionistic fuzzynumbers are pointed out. The proposed method consider not only the rank but also the decision maker optimisticattitude and it is shown that proposed ranking approach is more intuitive and reasonable as compared to existingranking approaches. Also the proposed ranking function satisfies the reasonable properties for the ordering of fuzzyquantity. For practical use, proposed ranking approach is applied to decision making problem.

Ranking fuzzy quantities based on the angle of the reference functions

Ordering fuzzy quantities and their comparison play a key tool in many applied models in the world and in particular decision-making procedures. However a huge number of researches is attracted to this filed but until now there is any unique accepted method to rank the fuzzy quantities. In fact, each proposed method may has some shortcoming. So we are going to present a novel method based on the angle of the reference functions to cover a wide range of fuzzy quantities by over coming the draw backs of some existing methods. In the mentioned firstly, the angle between the left and right membership functions (the reference functions) of every fuzzy set is called Angle of Fuzzy Set (AFS), and then in order to extend ranking of two fuzzy sets the angle of fuzzy sets and α-cuts is used. The method is illustrated by some numerical examples and in particular the results of ranking by the proposed method and some common and existing methods for ranking fuzzy sets is compared to verify the advantage of the new approach. In particular, based on the results of comparison of our method with well known methods which are exist in the literature, we will see that against of most existing ranking approaches, our proposed approach can rank fuzzy numbers that have the same mode and symmetric spreads. In fact, the proposed method in this paper can effectively rank symmetric fuzzy numbers as well as the effective methods which are appeared in the literature. Moreover, unlike of most existing ranking approaches, our proposed approach can rank non-normal fuzzy sets. Finally, we emphasize that the concept of fuzzy ordering is one of key role in establishing the numerical algorithms in operations research such as fuzzy primal simplex algorithms, fuzzy dual simplex algorithms and as well as discussed in the works of Ebrahimnejad and Nasseri and coworkers [1–7].

Ranking on Data Manifold with Sink Points

Ranking is an important problem in various applications, such as Information Retrieval (IR), natural language processing, computational biology, and social sciences. Many ranking approaches have been proposed to rank objects according to their degrees of relevance or importance. Beyond these two goals, diversity has also been recognized as a crucial criterion in ranking. Top ranked results are expected to convey as little redundant information as possible, and cover as many aspects as possible. However, existing ranking approaches either take no account of diversity, or handle it separately with some heuristics. In this paper, we introduce a novel approach, Manifold Ranking with Sink Points (MRSPs), to address diversity as well as relevance and importance in ranking. Specifically, our approach uses a manifold ranking process over the data manifold, which can naturally find the most relevant and important data objects. Meanwhile, by turning ranked objects into sink points on data manifold, we can effectively prevent redundant objects from receiving a high rank. MRSP not only shows a nice convergence property, but also has an interesting and satisfying optimization explanation. We applied MRSP on two application tasks, update summarization and query recommendation, where diversity is of great concern in ranking. Experimental results on both tasks present a strong empirical performance of MRSP as compared to existing ranking approaches.

A NEW RANKING APPROACH AND ITS APPLICATION TO SOLVING FUZZY CRITICAL PATH PROBLEMS

There are several methods for solving fuzzy critical path problems in which ranking approaches are used to compare fuzzy numbers. In this paper, some fuzzy critical path problems are chosen to show that the results obtained with fuzzy critical path methods that use existing ranking approaches are not appropriate according to real life situations. To obtain appropriate results, a new ranking approach the Mehar ranking approach, is proposed for comparing LR flat fuzzy numbers. To show the advantages of the Mehar ranking approach over existing approaches, selected fuzzy critical path problems are solved by using the existing methods together with the Mehar ranking approach. It is shown that the obtained results are appropriate.

Pest: Fast approximate keyword search in semantic data using eigenvector-based term propagation

We present pest, a novel approach to the approximate querying of graph-structured data such as RDF that exploits the data's structure to propagate term weights between related data items. We focus on data where meaningful answers are given through the application semantics, e.g., pages in wikis, persons in social networks, or papers in a research network such as Mendeley. The pest matrix generalizes the Google Matrix used in PageRank with a term-weight dependent leap and accommodates different levels of (semantic) closeness for different relations in the data, e.g., friend vs. co-worker in a social network. Its eigenvectors represent the distribution of a term after propagation. The eigenvectors for all terms together form a (vector space) index that takes the structure of the data into account and can be used with standard document retrieval techniques. In extensive experiments including a user study on a real life wiki, we show how pest improves the quality of the ranking over a range of existing ranking approaches, yet achieves a query performance comparable to a plain vector space index.