Multi-attribute Weighting Research Articles

Trigonometric projection statistics histograms for 3D local feature representation and shape description

Feature representation as a significant approach to three-dimensional (3D) shape description has been widely employed in computer vision. However, most existing methods are suffering from the emerging challenges for descriptiveness, robustness and efficiency. This paper presents a novel feature descriptor named trigonometric projection statistics histograms (TPSH). By constructing the repeatable local reference frame based on a multi-attribute weighting strategy, TPSH can address many prevailing nuisances such as noise, occlusion and varying resolution. The trigonometric projection mechanism is originally proposed for TPSH generation, which combines two perspective views to encode both spatial distribution and geometrical measurements from local shape into statistics histograms. The experimental evaluation on public datasets proves that TPSH outperforms state-of-the-art methods in descriptiveness and robustness while maintaining storage compactness and computational efficiency. It is demonstrated that TPSH can not only be suited for 3D object recognition and shape registration, but also generalized across various acquisition devices, data modalities and application scenarios.

Novel Approach of Obtaining Dynamic Multi-attribute Weight for Intuitionistic Fuzzy Environment Based on Fractional Integrals

Although there are many methods to obtain attribute weights in dynamic fuzzy environment, most of them assume attribute weights to obey predetermined distribution functions or constraints, which make them impossible to accurately describe the decision-making process and get reasonable results. In order to solve this problem, fractional integral with memory and heredity is introduced. Decision-makers’ risk preference information and the amount of useful information given by attributes are both considered to effectively address dynamic multi-attribute decision-making problems in intuitionistic fuzzy. Then, a value function is designed to accurately describe the decision-makers’ risk preference information and a multi-stage dynamic reference point setting method for intuitionistic fuzzy environment is proposed. Next, a method for obtaining dynamic attribute weights for intuitionistic fuzzy environment is given. By considering the background information of alternatives, a dynamic psychological distance measure for intuitionistic fuzzy set is produced. Based on the proposed dynamic psychological distance measure and TOPSIS, a dynamic multi-attribute decision-making method for intuitionistic fuzzy environment is given. Finally, two examples are given to illustrate the effectiveness of the method and the influence of the parameters is analyzed.

A Method of Determining Multi-Attribute Weights Based on Single-Valued Neutrosophic Numbers and Its Application in TODIM

In this paper, the TODIM method is used to solve the multi-attribute decision-making problem with unknown attribute weight in venture capital, and the decision information is given in the form of single-valued neutrosophic numbers. In order to consider the objectivity and subjectivity of decision-making problems reasonably, the optimal weight is obtained by combining subjective weights and objective weights. Subjective weights are given directly by decision makers. Objective weights are obtained by establishing a weight optimization model with known decision information, then this method will compare with entropy weight method. These simulation results also validate the effectiveness and reasonableness of this proposed method.

PUMA: Parallel subspace clustering of categorical data using multi-attribute weights

There are two main reasons why traditional clustering schemes are incompetent for high-dimensional categorical data. First, traditional methods usually represent each cluster by all dimensions without difference; and second, traditional clustering methods only rely on an individual dimension of projection as an attribute’s weight ignoring relevance among attributes. We solve these two problems by a MapReduce-based subspace clustering algorithm (called PUMA) using multi-attribute weights. The attribute subspaces are constructed in our PUMA by calculating an attribute-value weight based on the co-occurrence probability of attribute values among different dimensions. PUMA obtains sub-clusters corresponding to respective attribute subspaces from each computing node in parallel. Lastly, PUMA measures various scale clusters by applying the hierarchical clustering method to iteratively merge sub-clusters. We implement PUMA on a 24-node Hadoop cluster. Experimental results reveal that using multi-attribute weights with subspace clustering can achieve better clustering accuracy on both synthetic and real-world high dimensional datasets. Experimental results also show that PUMA achieves high performance in terms of extensibility, scalability and the nearly linear speedup with respect to number of nodes. Additionally, experimental results demonstrate that PUMA is reasonable, effective, and practical to expert systems such as knowledge acquisition, word sense disambiguation, automatic abstracting and recommender systems.

An improved algorithm for personalized recommendation on MOOCs

Purpose In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users. Design/methodology/approach This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users. Findings The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is. Originality/value This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.

Incorporating equity in economic evaluations: a multi-attribute equity state approach

In publicly funded health care systems, decision makers must continually balance often conflicting priorities of efficiency and equity. Health economists have developed a set of highly sophisticated analytical methods for assessing efficiency, but less attention has been paid to formally incorporating equity considerations into analyses. As a result, where equity is considered is often informal, ad hoc and/or simplistic. This paper is a proposal for a mechanism for formally incorporating equity within the decision process. It begins with an overview of the current literature on equity weighting. It then considers the case of a single equity domain and illustrates how this is currently applied in practice by the UK’s National Institute for Health and Care Excellence. It then proposes a more comprehensive method for considering the multi-attribute equity state, where a population exhibits more than one trait considered worthy of differential weighting. Finally, the paper proposes a mechanism by which this could be applied in practice, and concludes with a discussion of the challenges for applying multi-attribute equity weighting.

Modeling project preferences in multiattribute portfolio decision analysis

When choosing a portfolio of projects with a multi-attribute weighting model, it is necessary to elicit trade-off statements about how important these attributes are relative to each other. Such statements correspond to weight constraints, and thus impact on which project portfolios are potentially optimal or non-dominated in view of the resulting set of feasible attribute weights. In this paper, we extend earlier preference elicitation approaches by allowing the decision maker to make direct statements about the selection and rejection of individual projects. We convert such project preference statements to weight information by determining the weights for which (i) the selected project is included in all potentially optimal or non-dominated portfolios, or (ii) the rejected project is not included in any potentially optimal or non-dominated portfolio. We prove that the two complementary selection rules will exclude exactly the same set of weights. However, analyses that apply the dominance structure often lead to multiple, mutually exclusive feasible weight sets, and therefore the approach based on potential optimality is more relevant for practical decision analysis. We also propose ex ante value of information measures to guide the elicitation of project preference statements, and illustrate our results by analyzing a real case on the selection of infrastructure maintenance projects.

Meta Scheduling Job Allocation for Reducing Workload in Cloud Service using Quality of Service

The cloud computing has become more complicated where the technology support increases in day to day basis. There are numerous strategies which had been measured for cloud computing but the huge amount parameter which affects the performance of cloud computing is scheduling and time complexity measures. The previous methods are focused towards scheduling jobs based on their priority measures. To propose a new scheduling approach this considers different attributes to schedule the workloads. The proposed method computes the multi-attribute weight for each job submitted based on meta-scheduling that will choose cloud servers which there are many jobs allotted but each has access to different resources. The importance is the availability of resource and utilization is highly performed in this proposal. We compute such a weight of job which is computed based on the access of attributes and their importance based on which the jobs are allocated. The proposed method has produced more scheduling performance and low time complexity.

Trade-offs between vehicular and pedestrian traffic using micro-simulation methods

A simple hypothetical network is analysed using the VISSIM micro-simulation model to study the effects of signal cycle timings on delay and travel time costs for both vehicles and pedestrians in various pedestrian phasing scenarios. To examine cost trade-offs between pedestrians and vehicles various multi-attribute weighting criteria are applied to different components of travel delay using relative values of time. Results show that the policy selection when considering pedestrians may differ from that when just considering vehicular traffic, which is currently standard practice. The multi-criteria analysis approach developed here makes it possible to optimise network performance and costs across all modes of travel.

Multiattribute decision aid with extended ISMAUT

Multiattribute decision-making problems with imprecise data refer to a situation in which at least one of the parameters such as attribute weights and value scores is not specified in precise numerical values. Often the imprecision of preference information, on one hand, may give a decision maker chances that are enhanced freedom of choice and comforts of specification and, on the other hand, may cause decision analysts difficulties in establishing dominance relations among alternatives. The model, imprecisely specified multiattribute utility theory (ISMAUT), developed by Sage and White in 1984, is a generalization of the standard multiattribute decision-analysis paradigm in that they extend the types of preference specifications and provide a novel approach to resolve the complication of a problem caused by imprecision on both attribute weights and value scores. This paper is intended to extend the ISMAUT in several aspects. For the first part, we present the properties of decision rules and their relationships in the presence of imprecise weight and value information in a systematical way though many research efforts, differing by respective problem domains considered, have been devoted to deal with them. Further, methods for resolving a nonlinearity inherent in the formulation while cutting into the number of linear programs to be solved are also presented. For the second part, a method for determining multiattribute weights is presented when paired comparison judgments on alternatives are articulated. The attribute weights are to be estimated in the direction of minimizing the amount of violations and thus to be as consistent as possible with a decision maker's a priori ordered pairs of alternatives. The derived multiattribute weights can be utilized for prioritizing the other alternatives that are not included in a set of a priori ordered pairs of alternatives. For the third part, the paper deals with a prescriptive group decision-making method by aggregating group members' imprecise preference judgments. The imprecise additive group value function can be decomposed into the individual decision maker's imprecise decision-making problems, which are finally aggregated to identify a group's preferred alternative. The group decision rules, analogous to the rules dealt in a single decision-making context, are presented as well

Using a Bayesian Approach to Quantify Scale Compatibility Bias

This paper proposes a new analytical framework to quantify and correct for scale compatibility bias in the assessment of trade-off weights in multiattribute value analysis. The procedure is demonstrated with an application to a fisheries management problem. Trade-off judgments are elicited from a group of fisheries experts with management responsibility in the Lake Erie basin. Then we use a Bayesian method to compute posterior probability distributions of attribute weights. In computing the Bayesian weights, our measurement model assumes that the weight ratios produced by each respondent's judgments are subject to random error and an unknown scale compatibility bias. Ratios are log-transformed and analyzed by a Bayesian linear model with a noninformative prior distribution. Posterior distributions are then developed for the weights and the bias. We estimate the compatibility bias for each person and, in most cases, it is found to be large and in the predicted direction, suggesting the importance of its consideration in deriving trade-off weights. In addition, the Bayesian framework is shown to be useful for quantifying the value of additional information about multiattribute weights. Finally, a simple heuristic procedure for assessing the weights appears to be effective in eliminating the bias.

THE EVALUATION OF IMPLEMENTATION IMPACT OF CENTRALIZED TRAFFIC CONTROL SYSTEMS IN RAILWAYS

In this article we propose to evaluate the utility of implementation of Centralized Traffic Control System EbiScreen using expert evaluations in indistinct numbers, in the form of trapezoid membership function, as we are confident in the advantages obtained using this evaluation method. The methodology of evaluating and weightng of criteria in a trapezoid membership form is proposed in this article together with the results of evaluation. The idea is to integrate the hierarchy of objectives with the multi-attribute weight vectors using the values obtained by the expert group in evaluation process. The additional weights for the attributes in the multi-attribute analysis are provided within the same scale, so that they can be combined to find the overall value of the evaluation. Methodological presentation is followed by the results of sample application in Lithuanian Railways where the object of evaluation was the EbiScreen Centralized Traffic Control System.

On the convergence of multiattribute weighting methods

The convergent validity of five multiattribute weighting methods is studied in an Internet experiment. This is the first experiment where the subjects created the alternatives and attributes themselves. Each subject used five methods to assess attribute weights – one version of the analytic hierarchy process (AHP), direct point allocation, simple multiattribute rating technique (SMART), swing weighting, and tradeoff weighting. They can all be used following the principles of multiattribute value theory. Furthermore, SMART, swing, and AHP ask the decision makers to give directly the numerical estimates of weight ratios although the elicitation questions are different. In earlier studies these methods have yielded different weights. Our results suggest that the resulting weights are different because the methods explicitly or implicitly lead the decision makers to choose their responses from a limited set of numbers. The other consequences from this are that the spread of weights and the inconsistency between the preference statements depend on the number of attributes that a decision maker considers simultaneously.

Preference ratios in multiattribute evaluation (PRIME)-elicitation and decision procedures under incomplete information

This paper presents the preference ratios in multiattribute evaluation (PRIME) method which supports the analysis of incomplete information in multiattribute weighting models. In PRIME, preference elicitation and synthesis is based on 1) the conversion of possibly imprecise ratio judgments into an imprecisely specified preference model, 2) the use of dominance structures and decision rules in deriving decision recommendations, and 3) the sequencing of the elicitation process into a series of elicitation tasks. This process may be continued until the most preferred alternative is identified or, alternatively, stopped with a decision recommendation if the decision maker is prepared to accept the possibility that the value of some other alternative is higher. An extensive simulation study on the computational properties of PRIME is presented. The method is illustrated with a re-analysis of an earlier case study on international oil tanker negotiations.

LP-metric sensitivity analysis for single and multi-attribute decision analysis

Analyzing the sensitivity of decisions to probability estimation error in single and multi-attribute problems and to errors in estimating additive multi-attribute value models in multi-attribute problems is an integral part of decision analysis. This paper presents an intuitive and tractable approach to this sensitivity analysis. Here a decision is considered insensitive if: 1) the probabilities or multi-attribute weights required for any other alternative to become preferred are not close to the original estimated probabilities and weights, and 2) the rank order of states implied by the probabilities or the rank order of attributes implied by the additive multi-attribute weights must change for any other alternative to become preferred. The sensitivity analysis is conducted using straight forward linear programming models. An example is used to demonstrate their application.

A New Interpretation of Alternative Pairwise. Comparisons for a Generalization of SMART

A new mathematical interpretation of alternative pairwise comparisons for a recently developed model of single-stage multiattribute decisionmaking and trade-off weight determination under uncertainty is presented. This model includes the following key features: 1) parameter values, i.e., lowest level attribute utility scores and trade-off weights, are described by set membership, and 2) use is made of directly expressed preferences among pairs of alternatives. The original mathematical interpretation of pairwise comparisons produced the following undesirable result: more precise lowest level attribute utility scores produce a more imprecise description of the trade-off weights. The new more conservative interpretation guarantees that more precise lowest level attribute utility scores will not increase the imprecision of the trade-off weights.

The role of task properties in determining the relative effectiveness of multiattribute weighting techniques

Most studies comparing the relative effectiveness of multiattribute weighting techniques have found few differences. These studies have, however, failed to systematically vary attribute properties such as number of attributes and distribution of correct attribute weights. It was hypothesized that any weighting technique would be more effective at arriving at the correct weights the smaller the number of attributes because of lessened information processing requirements. In addition, it was hypothesized that the relative effectiveness of different weighting techniques would depend on the peakedness of the distribution of correct attribute weights because different weighting techniques should generate more peaked distributions than others. Two experiments were conducted to test these hypotheses. The first experiment focused on the accuracy of the weights assigned to attributes by individuals; the second on the accuracy of groups. Both experiments confirmed the first hypothesis regarding the number of attributes, but only the first experiment confirmed the second hypothesis regarding the peakedness of the distribution of attribute weights.