Filter versus wrapper gene selection approaches in DNA microarray domains

By Iñaki Inza, Pedro Larrañaga, Rosa Blanco, Antonio J. Cerrolaza. Artif. Intell. Medicine, 2004

454 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6d51fbf6965a2c6a6caa08460cd247d9cc7ef606

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Molecular Biology

Citations in the corpus

A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, methodology): “This is the main advantage of wrapper methods, and has been shown to result in higher predictive performance than can be obtained with filter methods (Inza et al., 2004; Wah et al., 2018; Ghosh et al., 2020).”
Recent advances in feature selection and its applications (2017, background): “Nowadays, it has enjoyed increased attention due to the massive growth of data across many scientiﬁc disciplines, such as in genomic analysis [7], text mining [8], to name a few.”
Incremental wrapper-based gene selection from microarray data for cancer classification (2006, unknown): “We are specially interested in wrapper approach [6,16].”
Building interpretable fuzzy models for high dimensional data analysis in cancer diagnosis (2011, background): “More detailed discussions of these two approaches can be found in [9,20,22-24].”
A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue (2007, methodology): “In wrapper methods, the search for the good feature subset is conducted by using the classifier itself as a part of evaluation function [29,31].”
Statistical shape modeling of the left ventricle: myocardial infarct classification challenge (2017, methodology): “icant discriminatory information was selected from each local model using the P -metric feature selection method [63].”
Feature Selection for Classification: A Review (2014, methodology): “Based on this assumption, wrapper models utilize a specific classifier to evaluate the quality of selected features, and offer a simple and powerful way to address the problem of feature selection, regardless of the chosen learning machine [36, 26].”
SVM-RFE With MRMR Filter for Gene Selection (2010, unknown): “filter methods and wrapper methods [14].”
A review of microarray datasets and applied feature selection methods (2014, methodology): “Apart from the application of multivariate filter methods [34,126,121,45], the microarray problem was also treated with more complex techniques such as wrappers and embedded methods [22,63,60,97].”