A review of feature selection methods for machine learning-based disease risk prediction
Machine learning has shown utility in detecting patterns within large, unstructured, and
complex datasets. One of the promising applications of machine learning is in precision …
complex datasets. One of the promising applications of machine learning is in precision …
[HTML][HTML] Relief-based feature selection: Introduction and review
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 …
dimensionality in target problems and growing interest in advanced but computationally …
[HTML][HTML] Benchmark for filter methods for feature selection in high-dimensional classification data
Feature selection is one of the most fundamental problems in machine learning and has
drawn increasing attention due to high-dimensional data sets emerging from different fields …
drawn increasing attention due to high-dimensional data sets emerging from different fields …
A survey on ECG analysis
The electrocardiogram (ECG) signal basically corresponds to the electrical activity of the
heart. In the literature, the ECG signal has been analyzed and utilized for various purposes …
heart. In the literature, the ECG signal has been analyzed and utilized for various purposes …
Benchmark of filter methods for feature selection in high-dimensional gene expression survival data
A Bommert, T Welchowski, M Schmid… - Briefings in …, 2022 - academic.oup.com
Feature selection is crucial for the analysis of high-dimensional data, but benchmark studies
for data with a survival outcome are rare. We compare 14 filter methods for feature selection …
for data with a survival outcome are rare. We compare 14 filter methods for feature selection …
A performance-driven benchmark for feature selection in tabular deep learning
Academic tabular benchmarks often contain small sets of curated features. In contrast, data
scientists typically collect as many features as possible into their datasets, and even …
scientists typically collect as many features as possible into their datasets, and even …
Data mining: practical machine learning tools and techniques with Java implementations
Witten and Frank's textbook was one of two books that 1 used for a data mining class in the
Fall of 2001. The book covers all major methods of data mining that produce a knowledge …
Fall of 2001. The book covers all major methods of data mining that produce a knowledge …
Correlation-based feature selection for machine learning
MA Hall - 1999 - researchcommons.waikato.ac.nz
A central problem in machine learning is identifying a representative set of features from
which to construct a classification model for a particular task. This thesis addresses the …
which to construct a classification model for a particular task. This thesis addresses the …
A review of feature selection techniques in bioinformatics
Feature selection techniques have become an apparent need in many bioinformatics
applications. In addition to the large pool of techniques that have already been developed in …
applications. In addition to the large pool of techniques that have already been developed in …
Classification of hyperspectral remote sensing images with support vector machines
F Melgani, L Bruzzone - IEEE Transactions on geoscience and …, 2004 - ieeexplore.ieee.org
This paper addresses the problem of the classification of hyperspectral remote sensing
images by support vector machines (SVMs). First, we propose a theoretical discussion and …
images by support vector machines (SVMs). First, we propose a theoretical discussion and …