Pairwise comparison

About: Pairwise comparison is a research topic. Over the lifetime, 6804 publications have been published within this topic receiving 174081 citations.

IMPLEMENTATION OF GIS-BASED MULTICRITERIA DECISION ANALYSIS WITH VB IN ArcGIS

Abstract: This article focuses on the integration of multicriteria decision analysis (MCDA) and geographical information systems (GIS) and introduces a tool, GIS–MCDA, written in visual basic in ArcGIS for GIS-based MCDA. The GIS–MCDA deals with raster-based data sets and includes standardization, weighting and decision analysis methods, and sensitivity analysis. Simple additive weighting, weighted product method, technique for order preference by similarity to ideal solution, compromise programming, analytic hierarchy process, and ordered weighted average for decision analysis; ranking, rating, and pairwise comparison for weighting and linear scale transformation for standardization can be applied by using this tool. The maximum score and score range procedures can be used for linear scale transformation. In this article also an application of the GIS–MCDA to determine the flood vulnerability of the South Marmara Basin in Turkey is examined. To check the validity and reliability of the results, the flood vulnerability layer is compared with flood-affected areas.

Novel Approach in Decision Making with m–Polar Fuzzy ELECTRE-I

Abstract: ELECTRE-I technique is extensively used in real-life applications, but coping with the precise statistics and numerical measure, it is hard to target precisely because the expert’s judgments are mostly vague in practical problems. In this research article, we introduce an m–polar fuzzy ELECTRE-I (mF-ELECTRE-I) approach as a brand new extension of the ELECTRE-I, for multi-criteria decision-making problems based on m–polar fuzzy sets because it is a powerful tool for depicting fuzziness and uncertainty under multipolar information. Proposed technique is more flexible and practical for real-world problems, specially when data come from multipolar information. In the proposed approach, the ratings of the alternatives under subjective criteria and its normalized weights are assessed by the decision maker. We solve numerical examples to demonstrate the feasibility, validity and effectiveness of our proposed technique. We also make pairwise comparisons of the alternatives by using outranking relations in numerical examples. Further, we present the rankings of alternatives through a directed graph that shows which alternative is preferable or incomparable under m–polar fuzzy environment. Finally, we develop an algorithm of our proposed approach and compare it with previously existing approaches.

Analysing uncertainties of interval judgment data in multiple-criteria evaluation of forest plans

Abstract: The use of interval judgments instead of accurate pairwise comparisons has been proposed as a solution to facilitate the analysis of uncertainties in the widely applied pairwise comparisons technique. A method is presented for deriving probability distributions for the pairwise comparisons and for utilizing the distributions in the analysis of uncertainties in the evaluation process. The first step is that the expert or the decisionmaker is queried as to the best guess of the priority ratio of the attributes compared. This is followed by an adjusting query concerning the uncertainty in the comparison: what is the probability of the priority ratio being between the best guess ± 1 unit of the pairwise comparison scale? An application of the method is presented in the form of multiplecriteria evaluation of alternative management plans for a forest area.

Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening.

Abstract: This article is concerned with the problem of feature screening for multiclass linear discriminant analysis under ultrahigh-dimensional setting. We allow the number of classes to be relatively large. As a result, the total number of relevant features is larger than usual. This makes the related classification problem much more challenging than the conventional one, where the number of classes is small (very often two). To solve the problem, we propose a novel pairwise sure independence screening method for linear discriminant analysis with an ultrahigh-dimensional predictor. The proposed procedure is directly applicable to the situation with many classes. We further prove that the proposed method is screening consistent. Simulation studies are conducted to assess the finite sample performance of the new procedure. We also demonstrate the proposed methodology via an empirical analysis of a real life example on handwritten Chinese character recognition.

Non-Euclidean dissimilarities: causes and informativeness

Abstract: In the process of designing pattern recognition systems one may choose a representation based on pairwise dissimilarities between objects. This is especially appealing when a set of discriminative features is difficult to find. Various classification systems have been studied for such a dissimilarity representation: the direct use of the nearest neighbor rule, the postulation of a dissimilarity space and an embedding to a virtual, underlying feature vector space. It appears in several applications that the dissimilarity measures constructed by experts tend to have a non-Euclidean behavior. In this paper we first analyze the causes of such choices and then experimentally verify that the non-Euclidean property of the measure can be informative.