Search Results for author: Melissa Meeker

Found 2 papers, 1 papers with code

Relief-Based Feature Selection: Introduction and Review

no code implementations22 Nov 2017 Ryan J. Urbanowicz, Melissa Meeker, William LaCava, Randal S. Olson, Jason H. Moore

Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in advanced but computationally expensive methodologies able to model complex associations.

feature selection

Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Mining

2 code implementations22 Nov 2017 Ryan J. Urbanowicz, Randal S. Olson, Peter Schmitt, Melissa Meeker, Jason H. Moore

Modern biomedical data mining requires feature selection methods that can (1) be applied to large scale feature spaces (e. g. `omics' data), (2) function in noisy problems, (3) detect complex patterns of association (e. g. gene-gene interactions), (4) be flexibly adapted to various problem domains and data types (e. g. genetic variants, gene expression, and clinical data) and (5) are computationally tractable.

Benchmarking feature selection

Cannot find the paper you are looking for? You can Submit a new open access paper.