Selection of the Linearly Separable Feature Subsets

Bobrowski, Leon; Łukaszuk, Tomasz

doi:10.1007/978-3-540-24844-6_81

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…It is true that numerous data mining methods suffer from the so-called curse of dimensionality. Also neural networks have not been adapted for high-dimensional data; it can be rather recommended to use alternative approaches for a high-dimensional supervised classification (e. g. Bobrowski & Łukaszuk, 2011). 21 J.…”

Section: Resultsmentioning

confidence: 99%

Highly robust methods in data mining

Kalina¹

2013

Serb J Management

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Section: Resultsmentioning

confidence: 99%

Highly robust methods in data mining

Kalina¹

2013

Serb J Management

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“…scheme. We obtained them on each record of the dataset using an algorithm in the mentioned family of random subsampling, our SVM ensemble, and Genet [75], an algorithm that wisely adapts the training of a single SVM in a semi-automatic mode. The picture shows that there are records for which the error rate is high for every methods we consider.…”

Section: Numerical Resultsmentioning

confidence: 99%

Feature selection via Boolean independent component analysis

Apolloni

Bassis

Brega

2009

Information Sciences

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“…The feature subset with minimal CVE was selected as optimal and applied on data of all patients to determine receiver operating characteristic (ROC) curve and check classification accuracy. The details of RLS feature selection method were presented elsewhere .…”

Section: Methodsmentioning

confidence: 99%

Phenotypic features of vascular calcification in chronic kidney disease

et al. 2019

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Background Patients with chronic kidney disease stage 5 (CKD5) are predisposed to vascular calcification (VC), but the combined effect of factors associated with VC was sparsely investigated. We applied the relaxed linear separability (RLS) feature selection model to identify features that concomitantly associate with VC in CKD5 patients. Methods Epigastric arteries collected during surgery from living donor kidney transplant recipients were examined to score the histological extent of medial VC. Sixty‐two phenotypic features in 152 patients were entered into RLS model to differentiate between no–minimal VC (n = 93; score 0‐1) and moderate–extensive VC (n = 59; score 2‐3). The subset of features associated with VC was selected on the basis of cross‐validation procedure. The strength of association of the selected features with VC was expressed by the absolute value of ‘RLS factor’. Results Among 62 features, a subset of 17 features provided optimal prediction of VC with 89% of patients correctly classified into their groups. The 17 features included traditional risk factors (diabetes, age, cholesterol, BMI and male sex) and markers of bone metabolism, endothelial function, metabolites, serum antibodies and mitochondrial‐derived peptide. Positive RLS factors range from 1.26 to 4.05 indicating features associated with increased risk of VC, and negative RLS factors range from −0.95 to −1.83 indicating features associated with reduced risk of VC. Conclusion The RLS model identified 17 features including novel biomarkers and traditional risk factors that together concomitantly associated with medial VC. These results may inform further investigations of factors promoting VC in CKD5 patients.

show abstract

Selection of the Linearly Separable Feature Subsets

Cited by 10 publications

References 5 publications

Highly robust methods in data mining

Highly robust methods in data mining

Feature selection via Boolean independent component analysis

Phenotypic features of vascular calcification in chronic kidney disease

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