Relative entropy method for fuzzy multiple attribute decision making and its application to software quality evaluation

2014 ◽  
Vol 26 (4) ◽  
pp. 1687-1693 ◽  
Author(s):  
Qingxiang Li ◽  
Xiaofei Zhao ◽  
Rui Lin ◽  
Benyan Chen
Keyword(s):  
Decision Making ◽  
Relative Entropy ◽  
Software Quality ◽  
Quality Evaluation ◽  
Multiple Attribute Decision Making ◽  
Entropy Method ◽  
Software Quality Evaluation ◽  
Multiple Attribute ◽  
Relative Entropy Method

Model for Software Quality Evaluation with Fuzzy Information

2016 ◽  
Vol 13 (10) ◽  
pp. 7394-7398
Author(s):  
Yi-Ding Zhao ◽  
Zhi-Min Li ◽  
Xi-Guang Zhang
Keyword(s):  
Decision Making ◽  
Relative Entropy ◽  
Software Quality ◽  
Quality Evaluation ◽  
Multiple Attribute Decision Making ◽  
Triangular Fuzzy Number ◽  
Ideal Solution ◽  
Software Quality Evaluation ◽  
Relative Closeness ◽  
Multiple Attribute

To study the problem of multiple attribute decision making in which the decision making information values are triangular fuzzy number, a relative entropy decision making method for software quality evaluation is proposed. Then, according to the concept of the relative entropy, the relative closeness degree is defined to determine the ranking order of all alternatives by calculating the relative entropy to both the fuzzy positive-ideal solution (FPIS) and fuzzy negative-ideal solution (FNIS) simultaneously. At last, a numerical example for software quality evaluation is provided to illustrate the proposed method. The result shows the approach is simple, effective and easy to calculate.

A COMBINATION WEIGHTING ALGORITHM USING RELATIVE ENTROPY FOR DOCUMENT CLUSTERING

2014 ◽  
Vol 28 (03) ◽  
pp. 1453002 ◽  
Author(s):  
BO JI ◽  
YANGDONG YE ◽  
YU XIAO
Keyword(s):  
Relative Entropy ◽  
Document Clustering ◽  
Multiple Attribute Decision Making ◽  
Entropy Method ◽  
Data Object ◽  
Information Bottleneck ◽  
Subjective Weight ◽  
Multiple Attribute ◽  
Sequential Information ◽  
Relative Entropy Method

This paper proposes a combination weighting algorithm using relative entropy for document clustering. Combination weighting is widely used in multiple attribute decision making (MADM) problem. However, there exist two difficulties to hinder the applications of combination weighting on document clustering. First, combination weighting is based on the integration of subjective weighting and objective weighting. However, there are so many attributes in documents that the subjective weights which rely on manual annotation by experts are impracticable. Secondly, a document data object might contain hundreds or even thousands of features. It is an extremely time-consuming task to calculate the combination weights. To address the issues, we suggest to simplify the combination weighting by not distinguishing subjective weight and objective weight. Meanwhile, we choose relative entropy method to reduce running time. In our algorithm, we obtain a combination weight set with 14 combination forms. The experiments on real document data show that both on the AC/PR/RE measures and the mutual information (MI) measure, the proposed CWRE-sIB algorithm is superior to the original sequential information bottleneck (sIB) algorithm and a series of weighting-sIB algorithms, which are built by applying a single weighting scheme to the original sIB algorithm.

Optimize the Multi-Dimensional Test Coverage Based on Multiple Attribute Decision Making and Cross Entropy Method

2013 ◽  
Vol 365-366 ◽  
pp. 1336-1341
Author(s):  
Yi Zhang ◽  
Gang Wang ◽  
Ping Rong Lin
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Entropy Method ◽  
Cross Entropy ◽  
Test Scheme ◽  
Test Coverage ◽  
Multiple Attribute ◽  
Cross Entropy Method ◽  
Comprehensive Test ◽  
Scheme Selection

With the scale and complexity of software increasing, people's awareness of software quality assurance has gradually strengthened. How to carry out the test, test scheme optimization selection and how to improve the testing efficiency become the reality problems in software engineering. Using the test coverage of the multidimensional metric, the effectiveness of comprehensive test coverage, test coverage rate of satisfaction and test efficiency as the optimization test scheme selection, use of multiple attribute decision making lifting scheme selection algorithm to measure the optimal test program, ameliorate the severity of the past subjective experience dependent, so as to provide guidance for reasonable, effective and scientific test. The optimization of comprehensive test coverage scheme is applied to the Markov model of software testing; software testing with the average cost minimization as objective, using the cross entropy method the optimal coverage testing section to optimize the test process.

Entropy method of determining the attribute weights of interval numbers based on relative superiority

2021 ◽  
pp. 1-8
Author(s):  
Lin Li ◽  
Tiejun Ci ◽  
Xiaoyu Yang ◽  
Heng Du ◽  
Haocan Ma ◽  
...  
Keyword(s):  
Decision Making ◽  
Basic Principle ◽  
Multiple Attribute Decision Making ◽  
Entropy Method ◽  
Interval Numbers ◽  
Attribute Weights ◽  
Multiple Attribute ◽  
Multi Attribute Decision Making ◽  
Judgment Matrix ◽  
Train Of Thought

In view of the multi-attribute decision making problems which the attribute values are in the forms of interval numbers, the paper presents an entropy method to obtain the attribute weights using the relative superiority concept. Firstly, the concept of this kind of problem is explained; Then in the light of the basic principle of the traditional entropy value method and train of thought, it given the calculation steps of weights using the relative superiority about the attribute value is interval number multiple attribute decision making problems. Its core is that relative superiority judgment matrix is obtained by comparing with two sets of interval numbers under the same indicator, which the group of interval numbers is equivalently mapped to the exact value form with the merits of relationship, then the weights of each indicator are calculated. Finally, the method is illustrated by giving an example.

scholarly journals Approaches to Multiple Attribute Decision-Making with Fuzzy Number Intuitionistic Fuzzy Information and Their Application to English Teaching Quality Evaluation

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhen Zhang ◽  
Pengfei Su
Keyword(s):  
Decision Making ◽  
Fuzzy Number ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Multiple Attribute Decision Making ◽  
English Teaching ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Maclaurin Symmetric Mean ◽  
Multiple Attribute

Many experts and scholars focus on the Maclaurin symmetric mean (MSM) operator, which can reflect the interrelationship among the multi-input arguments. It has been generalized to different fuzzy environments and put into use in various actual decision problems. The fuzzy number intuitionistic fuzzy numbers (FNIFNs) could well depict the uncertainties and fuzziness during the English teaching quality evaluation. And the English teaching quality evaluation is frequently viewed as the multiple attribute decision-making (MADM) issue. We expand the MSM equation with FNIFNs to propose the fuzzy number intuitionistic fuzzy MSM (FNIFMSM) equation and fuzzy number intuitionistic fuzzy weighted MSM (FNIFWMSM) equation in this study. A few MADM tools are developed with FNIFWMSM equation. Finally, taking English teaching quality evaluation as an example, this paper illustrates the depicted approach.

An Improved Method for Solving Multi-Attribute Problem in Transportation Mode Decision Making Process

2014 ◽  
Vol 1030-1032 ◽  
pp. 1961-1965
Author(s):  
Bao Shan Lin ◽  
Yan Bin Fan
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Entropy Method ◽  
Topsis Method ◽  
Transportation Mode ◽  
Multiple Attribute ◽  
Decision Making Problem ◽  
Multi Attribute Decision Making ◽  
Time Price ◽  
Transportation Activity

In logistics activities, the choice of the transportation mode involves time, price, safety, and many other factors, it is a typical multi-attribute decision making problem. Traditionally, we usually adopt a certain decision-making method to solve such problems, but the resulting solution is often difficult to achieve our desired results. This article tries to combine AHP, information entropy method and the TOPSIS method to solve the multiple-attribute decision making problems in the process of transportation activity, and finally a case is given.

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