About: Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets

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type	http://eprints.org/ontology/ArticleEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #72582 Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets
sameAs	Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets
http://eprints.org/ontology/hasAccepted	Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets (PDF)
http://eprints.org/ontology/hasDocument	Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets (PDF) Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets (Other) Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets (Other) Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets (Other) Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets (Other) Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets (Other)
dc:hasVersion	Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets (PDF)
Title	Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets
described by	https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-01-09
Creator	A. Freitas
status	peer reviewed published
Publisher	Oxford University Press
abstract	An important problem in bioinformatics consists of identifying the most important features (or predictors), among a large number of features in a given classification dataset. This problem is often addressed by using a machine learning-based feature ranking method to identify a small set of top-ranked predictors (i.e. the most relevant features for classification). The large number of studies in this area have, however, an important limitation: they ignore the possibility that the top-ranked predictors occur in an instance of Simpson’s paradox, where the positive or negative association between a predictor and a class variable reverses sign upon conditional on each of the values of a third (confounder) variable. In this work, we review and investigate the role of Simpson’s paradox in the analysis of top-ranked predictors in high-dimensional bioinformatics datasets, in order to avoid the potential danger of misinterpreting an association between a predictor and the class variable. We perform computational experiments using four well-known feature ranking methods from the machine learning field and five high-dimensional datasets of ageing-related genes, where the predictors are Gene Ontology terms. The results show that occurrences of Simpson’s paradox involving top-ranked predictors are much more common for one of the feature ranking methods.
Is Part Of	Briefings in Bioinformatics https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/72582#authors
issue	2
volume	21
is topic of	https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #72582 Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets

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