A simple method to improve the consistency ratio of the pair-wise comparison matrix in ANP

Ergu, Daji; Kou, Gang; Peng, Yi; Shi, Yong

doi:10.1016/j.ejor.2011.03.014

Cited by 216 publications

(119 citation statements)

References 49 publications

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“…41 Many MCDM methods have been developed and applied in a wide variety of applications. 7,8,14,19,20,23,32,48 MCDM methods rank alternatives using different approaches. Applying various MCDM methods to a sorting problem is beneficial because the ranking agreed by several MCDM methods is more trustful than one generated by single MCDM method.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Classification Algorithms Using McDm and Rank Correlation

Kou

Peng

et al. 2012

Int. J. Info. Tech. Dec. Mak.

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521

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Classification algorithm selection is an important issue in many disciplines. Since it normally involves more than one criterion, the task of algorithm selection can be modeled as multiple criteria decision making (MCDM) problems. Different MCDM methods evaluate classifiers from different aspects and thus they may produce divergent rankings of classifiers. The goal of this paper is to propose an approach to resolve disagreements among MCDM methods based on Spearman's rank correlation coefficient. Five MCDM methods are examined using 17 classification algorithms and 10 performance criteria over 11 public-domain binary classification datasets in the experimental study. The rankings of classifiers are quite different at first. After applying the proposed approach, the differences among MCDM rankings are largely reduced. The experimental results prove that the proposed approach can resolve conflicting MCDM rankings and reach an agreement among different MCDM methods.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Classification Algorithms Using McDm and Rank Correlation

Kou

Peng

et al. 2012

Int. J. Info. Tech. Dec. Mak.

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521

173

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“…Multiple criteria decision making (MCDM) aims at solving decision problems with multiple objectives and often conflictive constraints [10][11][12][13]. Algorithm evaluation or selection normally needs to examine more than one criterion and can be modeled as MCDM problems [9].…”

Section: Mcdm Methodsmentioning

confidence: 99%

An Integrated Feature Selection and Classification Scheme

Peng¹,

Kou²,

Ergu³

et al. 2012

SIC

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Abstract：Irrelevant and redundant features may not only deteriorate the performances of classifiers, but also slow the prediction process. Another problem in prediction is the availability of a large number of classification models. How to choose a satisfactory classifier is an important yet understudied task. The goal of this paper is to propose an integrated scheme for feature selection and classifier evaluation in the context of prediction. It combines traditional feature selection techniques and multi-criteria decision making (MCDM) methods in an attempt to increase the accuracies of classification models and identify appropriate classifiers for different types of data sets. space, the algorithm space, and the performance measures. The algorithm selection was presented as a learning task by the machine learning community [7]. Data mining field suggested multi-criteria based metrics to compare classification algorithms [8]. Rokach [9] suggested that the algorithm selection can be considered as a multiple criteria decision making (MCDM) problem and MCDM methods can be utilized to systematically choose the appropriate algorithm. Peng et al. Keywords: multi-criteria decision making (MCDM);[4] applied a set of MCDM methods to rank classification algorithms for the task of software defect detection. Research MethodologyThis section presents the research scheme and the major components of the scheme, including feature selection methods, MCDM methods, and classification algorithms.Based on the findings of Myrtveit et al. [2], this study designs the research scheme with carful consideration of these three factors. First, multiple datasets, representing different sizes and domains, are selected for the experimental study. Second, five accuracy indicators are used to evaluate classifiers. Third, 10-fold cross-validation technique is applied to the sample datasets to select features. The research scheme is summarized in Figure 1. Classification ClassificationCompare results Figure 1. Research schemeThe datasets are handled by two different approaches. The first approach applies traditional feature selection and classification algorithms to the datasets to get prediction results. In the second approach, feature selection and classification are conducted in four steps. First, feature selection is conducted using traditional techniques. Features are then ranked using the proposed feature selection method. The third step employs MCDM methods to evaluate feature selection techniques and choose the better performed techniques. In the last step, the selected features are used in the classification. The classification results of the first and second approaches are compared to examine whether the proposed feature selection and MCDM methods can improve the prediction accuracy. The performances of classifiers are also evaluated using MCDM methods and a recommendation of classifiers for prediction is made based on their accuracy and reliability. Proposed feature selection methodsAs discussed in section 2, filter and wrapper are tw...

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“…Aguarón et al [30] and Escobar et al [31] proposed a geometric consistency index for AHP. Many other methods for measuring consistency have also been proposed Ergu et al [32]; Lin et al [33,34]; Lin and Kou [35].etc). If consistency is present, there are many derivation methods for priority vectors, including the eigenvector method (Saaty [36]), weighted least squares method (Chu, Kalaba, and Spingarn [37]), logarithmic least squares method (Crawford and Williams [38]), a heuristic approach (Lin et al [39]), and the cosine maximization method (Kou and Lin [40], etc).…”

Section: Preliminariesmentioning

confidence: 99%

A Similarity Measure-based Optimization Model for Group Decision Making with Multiplicative and Fuzzy Preference Relations

Chao

Kou

2016

INT J COMPUT COMMUN

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Group decision making (GDM) problem based on different preference relations aims to obtain a collective opinion based on various preference structures provided by a group of decision makers (DMs) or experts, those who have varying backgrounds and interests in real world. The decision process in proposed question includes three steps: integrating varying preference structures, reaching consensus opinion, selecting the best alternative. Two major approaches: preference transformation and optimization methods have been developed to deal with the issue in first step. However, the transformation processes causes information lose and existing optimization methods are so computationally complex that it is not easy to be used by management practice. This study proposes a new consistency-based method to integrate multiplicative and fuzzy preference relations, which is based on a cosine similarity measure to derive a collective priority vector. The basic idea is that a collective priority vector should be as similar per column as possible to a pairwise comparative matrix (PCM) in order to assure the group preference has highest consistency for each decision makers. The model is computationally simple, because it can be solved using a Lagrangian approach and obtain a collective priority vector following four simple steps. The proposed method can further used to derive priority vector of fuzzy AHP. Using three illustrative examples, the effectiveness and simpleness of the proposed model is demonstrated by comparison with other methods. The results show that the proposed model achieves the largest cosine values in all three examples, indicating the solution is the nearest theoretical perfectly consistent opinion for each decision makers.

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A simple method to improve the consistency ratio of the pair-wise comparison matrix in ANP

Cited by 216 publications

References 49 publications

Evaluation of Classification Algorithms Using McDm and Rank Correlation

Evaluation of Classification Algorithms Using McDm and Rank Correlation

An Integrated Feature Selection and Classification Scheme

A Similarity Measure-based Optimization Model for Group Decision Making with Multiplicative and Fuzzy Preference Relations

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