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Artificial Intelligence Review

, Volume 50, Issue 2, pp 201–240 | Cite as

An empirical evaluation of hierarchical feature selection methods for classification in bioinformatics datasets with gene ontology-based features

Article

Abstract

Hierarchical feature selection is a new research area in machine learning/data mining, which consists of performing feature selection by exploiting dependency relationships among hierarchically structured features. This paper evaluates four hierarchical feature selection methods, i.e., HIP, MR, SHSEL and GTD, used together with four types of lazy learning-based classifiers, i.e., Naïve Bayes, Tree Augmented Naïve Bayes, Bayesian Network Augmented Naïve Bayes and k-Nearest Neighbors classifiers. These four hierarchical feature selection methods are compared with each other and with a well-known “flat” feature selection method, i.e., Correlation-based Feature Selection. The adopted bioinformatics datasets consist of aging-related genes used as instances and Gene Ontology terms used as hierarchical features. The experimental results reveal that the HIP (Select Hierarchical Information Preserving Features) method performs best overall, in terms of predictive accuracy and robustness when coping with data where the instances’ classes have a substantially imbalanced distribution. This paper also reports a list of the Gene Ontology terms that were most often selected by the HIP method.

Keywords

Hierarchical feature selection Classification Machine learning Data mining Bayesian classifiers K-Nearest Neighbors Biology of aging 

Notes

Acknowledgements

We thank Dr. João Pedro de Magalhães for his valuable general advice on the biology of aging for this Project. We also thank Pablo Silva for providing an implementation code of the SHSEL method. We also acknowledge the support of concurrency researchers at the University of Kent for access to the ‘CoSMoS’ cluster, funded by EPSRC Grants EP/E049419/1 and EP/E0535/1.

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Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity College LondonLondonUK
  2. 2.School of ComputingUniversity of KentCanterburyUK

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