New Feature Selection Algorithm Based on Feature Stability and Correlation

Al-Shalabi, Luai

doi:10.1109/access.2022.3140209

Cited by 7 publications

(2 citation statements)

References 48 publications

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“…There are many feature selection algorithms used in the field of data science, data mining, machine learning, and others. The reader may refer to [26,27] for some details. The rough set theory (RS) is one of these algorithms.…”

Section: The Rough Set Theorymentioning

confidence: 99%

Evaluation of COVID-19 Vaccine Refusal among AOU Students in Kuwait and their Families and their Expected Inclination Towards the Acceptance or Refusal of the Vaccine

Al-Shalabi

2022

ijsmr

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The purpose of this research was to determine the factors influencing the refusal of a coronavirus disease (COVID- 19) vaccine among adult students from Arab Open University in Kuwait (AOU) and their families and to study the trends of reluctant participants. A questionnaire was conducted (n = 691; aged 12 and older). Significant factors and the tendency of hesitant participants to accept or reject the vaccine were explored by applying a cleaning and coding process, a rough set theory (RS), a decision tree (DT) classifier, and a p-value. Overall, 18.4% of the participants reported refusing to receive a COVID-19 vaccine, while 17.2% were uncertain. The study shows that hesitant subjects represent a tendency to accept vaccination. Of the vaccine-refusal participants, subjects aged 18-29, suffer from chronic disease, were infected with COVID-19, were vaccinated against seasonal flu, and had concerns about receiving a COVID-19, representing 44.1%, 21.05%, 16.76%, 54.33%, and 70.08%, respectively. Overall, 18.4% of the participants demonstrated a refusal to receive a COVID-19 vaccine and 17.2% are hesitant. Factors influencing the level of acceptance/rejection of the vaccine were determined. The results showed that hesitant participants have a strong tendency to accept the vaccine (81.82%). Since vaccination is an important strategy to reduce the spread of the COVID-19 pandemic, the ministry of public health must immediately address the significant factors for the acceptance/rejection of the vaccine, as well as the trend of hesitant participants toward the acceptance of the vaccine.

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Section: The Rough Set Theorymentioning

confidence: 99%

Evaluation of COVID-19 Vaccine Refusal among AOU Students in Kuwait and their Families and their Expected Inclination Towards the Acceptance or Refusal of the Vaccine

Al-Shalabi

2022

ijsmr

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“…However, the main challenge with these unsupervised approaches is hyper-dimensionality and the lack of unique solutions (15,16,17,18). An alternative approach is feature-selection with minimum redundancy, which selects the most relevant features and removes those that are collinear to the top selected features in a supervised manner (19,20,21,22,23). Both unsupervised latent variable and supervised minimum redundancy approaches have been applied in biomarker discovery, with supervised methods currently dominating the field.…”

Section: Introductionmentioning

confidence: 99%

Iterative Decorrelation Analysis, Unit of Measure Preserving Transformations and Latent Biomarker Discovery

Tamez-Peña

2023

Preprint

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Background Numerous biomarker discovery studies and exploratory clinical studies extract a large set of measurable variables, which often have varying degrees of correlation among them. This data collinearity can impact statistical model interpretation and hinder the discovery of potential associations between measured variables and the observed outcome. Exploratory Factor Analysis (EFA), Principal Component Analysis (PCA), and Machine-Learning (ML) can be used to discover latent variables associated with disease progression or outcome by computing transformation matrices, but the interpretation of unsupervised/supervised latent variables in high-dimensional datasets can be challenging. Results This study describe and reports the performance of the iterative decorrelation analysis algorithm (IDeA). The algorithm iteratively analyzes the correlation matrix of the data, updating the transformation coefficients until it reaches the desired correlation goal. The output of IDeA is a basis-transformation matrix that preserves the data dimensionality and unit of measure of the original observed variables. The main advantages of the IDeA basis transformations are sparsity and interpretability. The transformation does not alter uncorrelated features, thus statistical modeling and biomarker discovery in the new transformed basis can be a combination of novel latent variables and a sizable subset of unaltered variables. The algorithm was tested on five multidimensional/hyperdimensional and multimodal sets, demonstrating the effect of decorrelation parameters, interpretability, and latent biomarker discovery. Conclusions The iterative decorrelation approach provides a simple to use tool for researchers to explore the association between correlated features in hyperdimensional/multimodal settings and to decorrelate significant associations via latent variables that preserve the unit of measurement. An efficient computer implementation of IDeA is available in the FRESA.CAD R package (https://cran.r-project.org/web/packages/FRESA.CAD/index.html).

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Robust classification and biomarker discovery of inherited metabolic diseases using GC–MS urinary metabolomics analysis combined with chemometrics

Chen

Zhang

et al. 2023

Microchemical Journal

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New Feature Selection Algorithm Based on Feature Stability and Correlation

Cited by 7 publications

References 48 publications

Evaluation of COVID-19 Vaccine Refusal among AOU Students in Kuwait and their Families and their Expected Inclination Towards the Acceptance or Refusal of the Vaccine

Evaluation of COVID-19 Vaccine Refusal among AOU Students in Kuwait and their Families and their Expected Inclination Towards the Acceptance or Refusal of the Vaccine

Iterative Decorrelation Analysis, Unit of Measure Preserving Transformations and Latent Biomarker Discovery

Robust classification and biomarker discovery of inherited metabolic diseases using GC–MS urinary metabolomics analysis combined with chemometrics

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