Jamming Style Selection for Small Sample Radar Jamming Rule Base

2018 ◽  
Author(s):  
Xing Qiang ◽  
Zhu Wei-gang ◽  
Bo Yuan
Keyword(s):  
Small Sample ◽  
Rule Base ◽  
Selection For

scholarly journals Isolating The Key Variables For Regression Models In Enterprise Software Acquisition Decisions: A Blocking Technique

2011 ◽  
Vol 5 (8) ◽  
Author(s):  
Rolando Pena-Sanchez ◽  
Jacques Verville ◽  
Christine Bernadas
Keyword(s):  
Multivariate Regression ◽  
Regression Models ◽  
Stepwise Regression ◽  
Model Building ◽  
Small Sample ◽  
Coefficient Of Determination ◽  
Enterprise Software ◽  
Key Variables ◽  
Selection For ◽  
Relative Variability

<p class="MsoNormal" style="text-align: justify; margin: 0in 34.2pt 0pt 0.5in;"><span style="font-size: 10pt;"><span style="font-family: Times New Roman;">Often researchers in the field of information systems face problems related to the variable selection for model building; as well as difficulties associated to their data (small sample and/or non normality). The goal of this article is to present an original statistical blocking-technique based on relative variability for screening of variables in multivariate regression models. We applied the blocking-technique and a nonparametric bootstrapping method to the data collected on the <span style="text-decoration: underline;">USA-South border</span> for a research concerning enterprise software (ES) acquisition contracts. Three mutually exclusive blocks of relative variability for the response variables were formed and their corresponding regression models were built and explained. A conclusion was drawn about the decreasing tendency on the adjusted coefficient of determination (R<sup>2</sup><sub>adj</sub>) magnitudes when the blocks change from low (L) to high (H) condition of relative variability. The obtained models (via stepwise regression) exhibited significant p-values (0.0001).<span style="mso-bidi-font-weight: bold;"></span></span></span></p>

Motor Imagery EEG Classification with Biclustering Based Fuzzy Inference

2020 ◽  
Vol 10 (7) ◽  
pp. 1486-1493
Author(s):  
Jianjun Sun
Keyword(s):  
Fuzzy Inference ◽  
Feature Space ◽  
Small Sample ◽  
Majority Voting ◽  
Training Data ◽  
Rule Base ◽  
Data Sets ◽  
Common Spatial Pattern ◽  
Eeg Classification ◽  
Subsequent Step

The rehabilitation of armless or footless patients is of great importance. One choice for such issue is using the electroencephalograph (EEG) brain computer interface to help the patients communicate with outside. Classifying the EEG signals generated from mental activity is one of the most important technologies. However, existing classification methods often suffer the overfitting problem caused by the small training data sets while big dimensionality of feature space. Fuzzy inference can imitate the human judgement, effectively dealing with uncertainty and small-sample learning problems. Besides, biclustering has shown excellent performance in constructing rule base. This paper proposes a novel biclustering based fuzzy inference method for EEG classification. It can be divided into five steps. The first step is generating features with common spatial pattern. The second step is searching local coherent patterns with column nearly constant biclustering. The third step is to transform the patterns to if-then rules with column averaging and majority voting strategy. Subsequent step is to employ Mamdani fuzzy inference to map the input feature vector into decimals. Finally, particle swarm optimization is utilized to generate optimal threshold for linear classification. Experiments on several commonly used data sets show that the proposed method has advantages over competitors in terms of classification accuracy.

Wrapper feature selection for small sample size data driven by complete error estimates

2012 ◽  
Vol 108 (1) ◽  
pp. 138-150 ◽  
Author(s):  
Martin Macaš ◽  
Lenka Lhotská ◽  
Eduard Bakstein ◽  
Daniel Novák ◽  
Jiří Wild ◽  
...  
Keyword(s):  
Feature Selection ◽  
Sample Size ◽  
Error Estimates ◽  
Small Sample Size ◽  
Small Sample ◽  
Data Driven ◽  
Selection For ◽  
Wrapper Feature Selection ◽  
Size Data

scholarly journals sc-REnF: An Entropy Guided Robust Feature Selection for Single-Cell RNA-seq Data

2021 ◽  
Author(s):  
Snehalika Lall ◽  
Abhik Ghosh ◽  
Sumanta Ray ◽  
Sanghamitra Bandyopadhyay
Keyword(s):  
Single Cell ◽  
Gene Selection ◽  
Small Sample ◽  
Rna Seq ◽  
Homogeneous Grouping ◽  
Cell Clustering ◽  
Selection For ◽  
Downstream Analysis ◽  
Cell Data

Abstract Annotation of cells in single-cell clustering requires a homogeneous grouping of cell populations. Since single cell data is susceptible to technical noise, the quality of genes selected prior to clustering is of crucial importance in the preliminary steps of downstream analysis. Therefore, interest in robust gene selection has gained considerable attention in recent years. We introduce sc-REnF, (robust entropy based feature (gene) selection method), aiming to leverage the advantages of Rényi and Tsallis> entropies in gene selection for single cell clustering. Experiments demonstrate that with tuned parameter (q), Rényi and Tsallis entropies select genes that improved the clustering results significantly, over the other competing methods. sc-REnF can capture relevancy and redundancy among the features of noisy data extremely well due to its robust objective function. Moreover, the selected features/genes can able to clusters the unknown cells with a high accuracy. Finally, sc-REnF yields good clustering performance in small sample, large feature scRNA-seq data.

scholarly journals A note on model selection for small sample regression

2017 ◽  
Vol 106 (11) ◽  
pp. 1839-1862 ◽  
Author(s):  
Masanori Kawakita ◽  
Jun’ichi Takeuchi
Keyword(s):  
Model Selection ◽  
Small Sample ◽  
Selection For

scholarly journals Locus-Specific Genetic Differentiation at Rw Among Warfarin-Resistant Rat (Rattus norvegicus) Populations

Genetics ◽  
2003 ◽  
Vol 164 (3) ◽  
pp. 1055-1070
Author(s):  
Michael H Kohn ◽  
Hans-Joachim Pelz ◽  
Robert K Wayne
Keyword(s):  
Rattus Norvegicus ◽  
Small Sample ◽  
Supporting Evidence ◽  
Local Conditions ◽  
Specific Effects ◽  
Local Selection ◽  
Epoxide Reductase ◽  
Selection For ◽  
And Migration ◽  
Candidate Loci

Abstract Populations may diverge at fitness-related genes as a result of adaptation to local conditions. The ability to detect this divergence by marker-based genomic scans depends on the relative magnitudes of selection, recombination, and migration. We survey rat (Rattus norvegicus) populations to assess the effect that local selection with anticoagulant rodenticides has had on microsatellite marker variation and differentiation at the warfarin resistance gene (Rw) relative to the effect on the genomic background. Initially, using a small sample of 16 rats, we demonstrate tight linkage of microsatellite D1Rat219 to Rw by association mapping of genotypes expressing an anticoagulant-rodenticide-insensitive vitamin K 2,3-epoxide reductase (VKOR). Then, using allele frequencies at D1Rat219, we show that predicted and observed resistance levels in 27 populations correspond, suggesting intense and recent selection for resistance. A contrast of FST values between D1Rat219 and the genomic background revealed that rodenticide selection has overwhelmed drift-mediated population structure only at Rw. A case-controlled design distinguished these locus-specific effects of selection at Rw from background levels of differentiation more effectively than a population-controlled approach. Our results support the notion that an analysis of locus-specific population genetic structure may assist the discovery and mapping of novel candidate loci that are the object of selection or may provide supporting evidence for previously identified loci.

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