scholarly journals WEBIRA - Comparative Analysis of Weight Balancing Method

2017 ◽  
Vol 12 (2) ◽  
pp. 238 ◽  
Author(s):  
Aleksandras Krylovas ◽  
Natalja Kosareva ◽  
Edmundas Kazimieras Zavadskas
Keyword(s):  
Comparative Analysis ◽  
Optimization Problem ◽  
Entropy Method ◽  
Data Normalization ◽  
Normalization Methods ◽  
Criterion Weight ◽  
Simple Additive Weighting

The attributes weight establishing problem is one of the most important MCDM tasks. This study summarizes weight determining approach which is called WEBIRA (WEight Balancing Indicator Ranks Accordance). This method requires to solve complicated optimization problem and its application is possible by carrying out non trivial calculations. The efficiency of WEBIRA and other MCDM methods – SAW (Simple Additive Weighting) and EMDCW (Entropy Method for Determining the Criterion Weight) compared for 4 different data normalization methods. The results of the study revealed that more sophisticated WEBIRA method is significantly efficient for all considered numbers of alternatives. Efficiency of all methods decreases with increasing number of alternatives, but WEBIRA is still applicable, while application of other methods is impossible as the number of alternatives is greater than 11. WEBIRA is the least affected by the data normalization, while EMDCW is the most affected method.

ReS-Algorithm for Converting Normalized Values of Cost Criteria Into Benefit Criteria in MCDM Tasks

2020 ◽  
Vol 19 (05) ◽  
pp. 1389-1423
Author(s):  
Irik Z. Mukhametzyanov
Keyword(s):  
Performance Indicator ◽  
Multicriteria Decision Making ◽  
Measurement Scale ◽  
Multidimensional Data ◽  
Sorting Algorithm ◽  
Data Normalization ◽  
Basic Principles ◽  
Normalization Methods ◽  
The Cost ◽  
Invariant Properties

A review of modern methods of data normalization in the tasks of multicriteria decision-making and multidimensional classification is presented. The invariant properties of linear normalization methods are determined. Two basic principles of normalization of multidimensional data are defined: preservation of dispositions of natural and normalized values on the measurement scale and the absence of a displacement in the areas of normalized values of various criteria relative to each other. A method is proposed for converting normalized values of cost criteria to profit criteria based on the reverse sorting algorithm (ReS-algorithm). The ReS-algorithm preserves the dispositions of the natural and normalized values of the attributes of the alternatives and eliminates the displacement the areas of normalized values of the cost criteria relative to the profit criteria, which ensures the equality of the contributions of various criteria to the performance indicator of the alternatives.

scholarly journals A Cross Entropy Algorithm for Classification with $\delta$−Patterns

2006 ◽  
Vol DMTCS Proceedings vol. AG,... (Proceedings) ◽  
Author(s):  
Gabriela Alexe ◽  
Gyan Bhanot ◽  
Adriana Climescu-Haulica
Keyword(s):  
Optimization Problem ◽  
Genomic Data ◽  
Maximal Clique ◽  
Large Datasets ◽  
Entropy Method ◽  
Combinatorial Optimization Problem ◽  
Cross Entropy ◽  
Cross Entropy Method ◽  
International Audience ◽  
Generation Problem

International audience A classification strategy based on $\delta$-patterns is developed via a combinatorial optimization problem related with the maximal clique generation problem on a graph. The proposed solution uses the cross entropy method and has the advantage to be particularly suitable for large datasets. This study is tailored for the particularities of the genomic data.

scholarly journals NormExpression: an R package to normalize gene expression data using evaluated methods

2018 ◽  
Author(s):  
Zhenfeng Wu ◽  
Weixiang Liu ◽  
Xiufeng Jin ◽  
Deshui Yu ◽  
Hua Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Expression Profiles ◽  
R Package ◽  
Data Normalization ◽  
Expression Data ◽  
Normalization Methods ◽  
Normalize Gene Expression

AbstractData normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the current normalization methods, the different metrics yield inconsistent results. In this study, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods, achieving consistency in our evaluation results using both bulk RNA-seq and scRNA-seq data from the same library construction protocol. This consistency has validated the underlying theory that a sucessiful normalization method simultaneously maximizes the number of uniform genes and minimizes the correlation between the expression profiles of gene pairs. This consistency can also be used to analyze the quality of gene expression data. The gene expression data, normalization methods and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis.

scholarly journals EP-2051 A comparative analysis of MR signal normalization methods during proton therapy treatment

2019 ◽  
Vol 133 ◽  
pp. S1127-S1128
Author(s):  
G. Buizza ◽  
C. Paganelli ◽  
G. Fontana ◽  
A. Franconeri ◽  
M.V. Raciti ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Proton Therapy ◽  
Normalization Methods ◽  
Signal Normalization ◽  
Therapy Treatment

scholarly journals Development of a Common Oligonucleotide Reference Standard for Microarray Data Normalization and Comparison across Different Microbial Communities

2009 ◽  
Vol 76 (4) ◽  
pp. 1088-1094 ◽  
Author(s):  
Yuting Liang ◽  
Zhili He ◽  
Liyou Wu ◽  
Ye Deng ◽  
Guanghe Li ◽  
...  
Keyword(s):  
Microbial Communities ◽  
Microarray Data ◽  
Genomic Dna ◽  
Soil Microbial Communities ◽  
Functional Gene ◽  
Data Normalization ◽  
Reference Standard ◽  
Complementary Sequence ◽  
Normalization Methods ◽  
Reference Target

ABSTRACT High-density functional gene arrays have become a powerful tool for environmental microbial detection and characterization. However, microarray data normalization and comparison for this type of microarray remain a challenge in environmental microbiology studies because some commonly used normalization methods (e.g., genomic DNA) for the study of pure cultures are not applicable. In this study, we developed a common oligonucleotide reference standard (CORS) method to address this problem. A unique 50-mer reference oligonucleotide probe was selected to co-spot with gene probes for each array feature. The complementary sequence was synthesized and labeled for use as the reference target, which was then spiked and cohybridized with each sample. The signal intensity of this reference target was used for microarray data normalization and comparison. The optimal amount or concentration were determined to be ca. 0.5 to 2.5% of a gene probe for the reference probe and ca. 0.25 to 1.25 fmol/μl for the reference target based on our evaluation with a pilot array. The CORS method was then compared to dye swap and genomic DNA normalization methods using the Desulfovibrio vulgaris whole-genome microarray, and significant linear correlations were observed. This method was then applied to a functional gene array to analyze soil microbial communities, and the results demonstrated that the variation of signal intensities among replicates based on the CORS method was significantly lower than the total intensity normalization method. The developed CORS provides a useful approach for microarray data normalization and comparison for studies of complex microbial communities.

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