Verifying the fully “Laplacianised” posterior Naïve Bayesian approach and more
File(s)
Published version
Author(s)
Mussa, HY
Marcus, D
Mitchell, JBO
Glen, RC
Type
Journal Article
Abstract
Background
In a recent paper, Mussa, Mitchell and Glen (MMG) have mathematically demonstrated that the “Laplacian Corrected Modified Naïve Bayes” (LCMNB) algorithm can be viewed as a variant of the so-called Standard Naïve Bayes (SNB) scheme, whereby the role played by absence of compound features in classifying/assigning the compound to its appropriate class is ignored. MMG have also proffered guidelines regarding the conditions under which this omission may hold. Utilising three data sets, the present paper examines the validity of these guidelines in practice. The paper also extends MMG’s work and introduces a new version of the SNB classifier: “Tapered Naïve Bayes” (TNB). TNB does not discard the role of absence of a feature out of hand, nor does it fully consider its role. Hence, TNB encapsulates both SNB and LCMNB.
Results
LCMNB, SNB and TNB performed differently on classifying 4,658, 5,031 and 1,149 ligands (all chosen from the ChEMBL Database) distributed over 31 enzymes, 23 membrane receptors, and one ion-channel, four transporters and one transcription factor as their target proteins. When the number of features utilised was equal to or smaller than the “optimal” number of features for a given data set, SNB classifiers systematically gave better classification results than those yielded by LCMNB classifiers. The opposite was true when the number of features employed was markedly larger than the “optimal” number of features for this data set. Nonetheless, these LCMNB performances were worse than the classification performance achieved by SNB when the “optimal” number of features for the data set was utilised. TNB classifiers systematically outperformed both SNB and LCMNB classifiers.
Conclusions
The classification results obtained in this study concur with the mathematical based guidelines given in MMG’s paper—that is, ignoring the role of absence of a feature out of hand does not necessarily improve classification performance of the SNB approach; if anything, it could make the performance of the SNB method worse. The results obtained also lend support to the rationale, on which the TNB algorithm rests: handled judiciously, taking into account absence of features can enhance (not impair) the discriminatory classification power of the SNB approach.
In a recent paper, Mussa, Mitchell and Glen (MMG) have mathematically demonstrated that the “Laplacian Corrected Modified Naïve Bayes” (LCMNB) algorithm can be viewed as a variant of the so-called Standard Naïve Bayes (SNB) scheme, whereby the role played by absence of compound features in classifying/assigning the compound to its appropriate class is ignored. MMG have also proffered guidelines regarding the conditions under which this omission may hold. Utilising three data sets, the present paper examines the validity of these guidelines in practice. The paper also extends MMG’s work and introduces a new version of the SNB classifier: “Tapered Naïve Bayes” (TNB). TNB does not discard the role of absence of a feature out of hand, nor does it fully consider its role. Hence, TNB encapsulates both SNB and LCMNB.
Results
LCMNB, SNB and TNB performed differently on classifying 4,658, 5,031 and 1,149 ligands (all chosen from the ChEMBL Database) distributed over 31 enzymes, 23 membrane receptors, and one ion-channel, four transporters and one transcription factor as their target proteins. When the number of features utilised was equal to or smaller than the “optimal” number of features for a given data set, SNB classifiers systematically gave better classification results than those yielded by LCMNB classifiers. The opposite was true when the number of features employed was markedly larger than the “optimal” number of features for this data set. Nonetheless, these LCMNB performances were worse than the classification performance achieved by SNB when the “optimal” number of features for the data set was utilised. TNB classifiers systematically outperformed both SNB and LCMNB classifiers.
Conclusions
The classification results obtained in this study concur with the mathematical based guidelines given in MMG’s paper—that is, ignoring the role of absence of a feature out of hand does not necessarily improve classification performance of the SNB approach; if anything, it could make the performance of the SNB method worse. The results obtained also lend support to the rationale, on which the TNB algorithm rests: handled judiciously, taking into account absence of features can enhance (not impair) the discriminatory classification power of the SNB approach.
Date Issued
2015-06-12
Date Acceptance
2015-05-12
Citation
Journal of Cheminformatics, 2015, 7
ISSN
1758-2946
Publisher
Springer Verlag
Journal / Book Title
Journal of Cheminformatics
Volume
7
Copyright Statement
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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Subjects
Science & Technology
Physical Sciences
Technology
Chemistry, Multidisciplinary
Computer Science, Information Systems
Computer Science, Interdisciplinary Applications
Chemistry
Computer Science
Classification
Naive Bayes
Tapering
Features
CHEMOINFORMATICS
PERFORMANCE
Naïve Bayes
Publication Status
Published
Article Number
27