Sentence examples for machine feature selection from inspiring English sources

After normalization, batch effect elimination and integration, significantly differentially methylated genes and the best combination of the biomarkers were determined by the leave-one-out SVM (support vector machine) feature selection procedure.

We coupled diversity profiles with unsupervised (hierarchical clustering) and supervised (support vector machine and feature selection) machine learning approaches in order to correlate patients' immunological statuses with their B- and T-cell repertoire data.

The major contributions of this article are: 1. a new, deterministic localization methodology that does not rely on solving any sophisticated inverse dispersion problem and is an alternative to the stochastic localization methodology presented in (Locke and Paschalidis 2013);   2. a novel sensor placement methodology that stems from a machine learning feature selection procedure; and   3.

Moreover, as an additional supporting process, two machine learning feature selection techniques were run.

Thirdly, we apply a conventional machine learning feature selection to the initial set of attributes.

Currently, many machine learning feature selection methods applied to microarray data explicitly eliminate genes which are redundant, in terms of discriminative or predictive value, with an existing set [ 1- 8].

The authors had drawn conclusion that a novel association between combinations of SNPs and T2D in a Korean population can be achieved by using support vector machine based feature selection method.

An iterative development process, based on machine learning and feature selection has been utilised in the development of machine learning driven prognostic models.

State of the art machine learning and feature selection methods are utilised for the prognostic modelling purposes.

Proper parameter settings of support vector machine (SVM) and feature selection are of great importance to its efficiency and accuracy.

The MLDPS is a non knowledge-based/data driven prognostic system which is developed by applying machine learning and feature selection techniques on legacy patient datasets.