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

Freitas, Alex A. (2019) Investigating the Role of Simpson’s Paradox in the Analysis of Top-Ranked Features in High-Dimensional Bioinformatics Datasets. Briefings in Bioinformatics, . E-ISSN 1477-4054. (doi:10.1093/bib/bby126) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided)

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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.

Item Type: Article
DOI/Identification number: 10.1093/bib/bby126
Uncontrolled keywords: Gene Ontology, machine learning, classification, feature ranking, ageing-related genes
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Depositing User: Alex Freitas
Date Deposited: 18 Feb 2019 17:55 UTC
Last Modified: 22 Aug 2019 11:42 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/72582 (The current URI for this page, for reference purposes)
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