scholarly journals Pushing the Limits for Judgmental Consistency: Comparing Random Weighting Schemes with Expert Judgments

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Martin Yu ◽  
Nathan Kuncel
Keyword(s):  
Weighting Schemes ◽  
Random Weighting

scholarly journals Applications of Different Weighting Schemes to Improve Pathway-Based Analysis

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Sook S. Ha ◽  
Inyoung Kim ◽  
Yue Wang ◽  
Jianhua Xuan
Keyword(s):  
Pathway Analysis ◽  
Molecular Classification ◽  
Weighting Schemes ◽  
Optimal Weights ◽  
Random Weights ◽  
Random Weighting ◽  
Classification Of Diseases ◽  
Disease Related Genes ◽  
Simulated Gene Expression

Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proved incorrect, and applying uniform weight in the pathway analysis may not be an appropriate approach for the tasks like molecular classification of diseases, as genes in a functional group may have different predicting power. Hence, we propose to use different weights to genes in pathway-based analysis and devise four weighting schemes. We applied them in two existing pathway analysis methods using both real and simulated gene expression data for pathways. Among all schemes, random weighting scheme, which generates random weights and selects optimal weights minimizing an objective function, performs best in terms ofPvalue or error rate reduction. Weighting changes pathway scoring and brings up some new significant pathways, leading to the detection of disease-related genes that are missed under uniform weight.

Climate Index Weighting Schemes for NWS ESP-Based Seasonal Volume Forecasts

2004 ◽  
Vol 5 (6) ◽  
pp. 1076-1090 ◽  
Author(s):  
Kevin Werner ◽  
David Brandon ◽  
Martyn Clark ◽  
Subhrendu Gangopadhyay
Keyword(s):  
Nearest Neighbor ◽  
Southern Oscillation ◽  
Climate Index ◽  
Climate Information ◽  
Streamflow Prediction ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Weighting Schemes ◽  
Temperature And Precipitation ◽  
Spring Runoff

Abstract This study compares methods to incorporate climate information into the National Weather Service River Forecast System (NWSRFS). Three small-to-medium river subbasins following roughly along a longitude in the Colorado River basin with different El Niño–Southern Oscillation signals were chosen as test basins. Historical ensemble forecasts of the spring runoff for each basin were generated using modeled hydrologic states and historical precipitation and temperature observations using the Ensemble Streamflow Prediction (ESP) component of the NWSRFS. Two general methods for using a climate index (e.g., Niño-3.4) are presented. The first method, post-ESP, uses the climate index to weight ensemble members from ESP. Four different post-ESP weighting schemes are presented. The second method, preadjustment, uses the climate index to modify the temperature and precipitation ensembles used in ESP. Two preadjustment methods are presented. This study shows the distance-sensitive nearest-neighbor post-ESP to be superior to the other post-ESP weighting schemes. Further, for the basins studied, forecasts based on post-ESP techniques outperformed those based on preadjustment techniques.

scholarly journals A new family of dissimilarity metrics for discrete character matrices that include inapplicable characters and its importance for disparity studies

2018 ◽  
Vol 285 (1892) ◽  
pp. 20181784 ◽  
Author(s):  
Melanie J. Hopkins ◽  
Katherine St John
Keyword(s):  
Phylogenetic Analysis ◽  
Missing Data ◽  
Rank Order ◽  
Software Tools ◽  
Weighting Schemes ◽  
New Family ◽  
Discrete Character ◽  
Secondary Characters

The use of discrete character data for disparity analyses has become more popular, partially due to the recognition that character data describe variation at large taxonomic scales, as well as the increasing availability of both character matrices co-opted from phylogenetic analysis and software tools. As taxonomic scope increases, the need to describe variation leads to some characters that may describe traits not found across all the taxa. In such situations, it is common practice to treat inapplicable characters as missing data when calculating dissimilarity matrices for disparity studies. For commonly used dissimilarity metrics like Wills's GED and Gower's coefficient, this can lead to the reranking of pairwise dissimilarities, resulting in taxa that share more primary character states being assigned larger dissimilarity values than taxa that share fewer. We introduce a family of metrics that proportionally weight primary characters according to the secondary characters that describe them, effectively eliminating this problem, and compare their performance to common dissimilarity metrics and previously proposed weighting schemes. When applied to empirical datasets, we confirm that choice of dissimilarity metric frequently affects the rank order of pairwise distances, differentially influencing downstream macroevolutionary inferences.

scholarly journals Improving Term Weighting Schemes for Short Text Classification in Vector Space Model

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 166578-166592
Author(s):  
Surender Singh Samant ◽  
N. L. Bhanu Murthy ◽  
Aruna Malapati
Keyword(s):  
Vector Space ◽  
Text Classification ◽  
Vector Space Model ◽  
Term Weighting ◽  
Weighting Schemes ◽  
Short Text ◽  
Space Model

scholarly journals Evolving weighting schemes for the Bag of Visual Words

2016 ◽  
Vol 28 (5) ◽  
pp. 925-939 ◽  
Author(s):  
Hugo Jair Escalante ◽  
Víctor Ponce-López ◽  
Sergio Escalera ◽  
Xavier Baró ◽  
Alicia Morales-Reyes ◽  
...  
Keyword(s):  
Bag Of Visual Words ◽  
Weighting Schemes ◽  
Visual Words
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